Applications of Trajectory Data in Transportation: Literature Review and Maryland Case Study
---
paper_title: ESTIMATING ORIGIN-DESTINATION MATRICES FROM ROADSIDE SURVEY DATA

paper_content:

The purpose of this study is to develop a valid and efficient method for estimating origin-destination tables from roadside survey data. Roadside surveys, whether conducted by interviews or postcard mailback methods, typically have in common the sampling of trip origin and destination information at survey stations. These survey stations are generally located where roads cross "screenlines," which are imaginary barriers drawn to intercept the trip types of interest.Such surveys also include counts of traffic volumes, by which the partial origin-destination (O-D) tables obtained at the different stations can be expanded and combined to obtain the complete O-D table which represents travel throughout the entire study area. The procedure used to expand the sample O-D information from the survey stations must recognize and deal appropriately with a number of problems: 1. (i) The "double counting" problem: Long-distance trips may pass through more than one survey station location; thus certain trips have the possibility of being sampled and expanded more than once, leading to a potentially serious overrepresentation of long-distance trips in the complete expanded trip table. 2. (ii) The "leaky screenline" problem: Some route choices, particularly those using very lightly traveled roads, may miss the survey stations entirely, leading to an underestimation of certain O-D patterns, or to distorted estimates if such sites are arbitrarily coupled with actual nearby station locations. 3. (iii) The efficient use of the data: There is a need to adjust expansion factors to compensate for double counting and leaky screenlines. How can this be accomplished such that all of the data obtained at the stations are used without loss of information? 4. (iv) The consequences of uncertainty and unknown travel behavior: Since the O-D data and other sampled variables are subject to random error, and since in general the probability of encountering a long-distance trip at some survey stations is affected by traveler route-choice behavior, which is not understood, the sample expansion procedure must rely on the use of erroneous input data and questionable assumptions. The preferred procedure must minimize, rather than amplify, the effects of such input errors. Here, five alternate methods for expanding roadside survey data in an unbiased manner are proposed and evaluated. In all cases, it is assumed that traveler route choice generally follows the pattern described by Dial's multipath assignment method. All methods are applied to a simple hypothetical network in order to examine their efficiency and error amplification properties. The evaluation of the five methods reveals that their performance properties vary considerably and that no single method is best in all circumstances. A microcomputer program has been provided as a tool to facilitate comparison among methods and to select the most appropriate expansion method for a particular application.
---
paper_title: The most likely trip matrix estimated from traffic counts

paper_content:

For a large number of applications conventional methods for estimating an origin destination matrix become too expensive to use. Two models, based on information minimisation and entropy maximisation principles, have been developed by the authors to estimate an O-D matrix from traffic counts. The models assume knowledge of the paths followed by the vehicles over the network. The models then use the traffic counts to estimate the most likely O-D matrix consistent with the link volumes available and any prior information about the trip matrix. Both models can be used to update and improve a previous O-D matrix. An algorithm to find a solution to the model is then described. The models have been tested with artificial data and performed reasonably well. Further research is being carried out to validate the models with real data.
---
paper_title: Development of origin–destination matrices using mobile phone call data

paper_content:

Abstract In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.
---
paper_title: ESTIMATION OF AN ORIGIN-DESTINATION MATRIX WITH RANDOM LINK CHOICE PROPORTIONS: A STATISTICAL APPROACH

paper_content:

The use of statistical modeling in the estimation of Origin-Destination (OD) matrix from traffic counts is reviewed. In particular, statistical models that consider explicitly the presence of measurement and sampling errors in the observed link flows are discussed. This paper proposes treating the link choice proportions as random variables. Accordingly, new statistical models are formulated and the corresponding Maximum Likelihood Estimator and Bayesian Estimator of the OD matrix are developed. The accuracies of these estimators are compared with those obtained by previous methods.
---
paper_title: Nonresponse Rates and Nonresponse Bias in Household Surveys

paper_content:

Many surveys of the U.S. household population are experiencing higher refusal rates. Nonresponse can, but need not, induce nonresponse bias in survey estimates. Recent empirical findings illustrate cases when the linkage between nonresponse rates and nonresponse biases is absent. Despite this, professional standards continue to urge high response rates. Statistical expressions of nonresponse bias can be translated into causal models to guide hypotheses about when nonresponse. causes bias. Alternative designs to measure nonresponse bias exist, providing different but incomplete information about the nature of the bias. A synthesis of research studies estimating nonresponse bias shows the bias often present. A logical question at this moment in history is what advantage probability sample surveys have if they suffer from high nonresponse rates. Since postsurvey adjustment for nonresponse requires auxiliary variables, the answer depends on the nature of the design and the quality of the auxiliary variables.
---
paper_title: Feasibility and Benefits of Advanced Four-Step and Activity-Based Travel Demand Models for Maryland

paper_content:

This research identifies current and emerging transportation planning policy issues faced by the Maryland State Highway Administration (SHA) and other State agencies. After reviewing the recently-developed Maryland Statewide Transportation Model (MSTM), the University of Maryland (UMD) research team summarizes the capability of the MSTM to analyze these planning and policy issues and prioritizes various model improvement needs. Based on identified planning and policy analysis needs, a thorough synthesis of existing transportation data sources, and proposed MSTM applications in Maryland, a Strategic MSTM Improvement Plan (SMIP) is developed to guide short- and long-run MSTM improvement and applications. This research also explores the feasibility of incorporating departure time choices into the MSTM by developing a prototype time-of-day choice model.
---
paper_title: The path most traveled: Travel demand estimation using big data resources

paper_content:

Abstract Rapid urbanization is placing increasing stress on already burdened transportation infrastructure. Ubiquitous mobile computing and the massive data it generates presents new opportunities to measure the demand for this infrastructure, diagnose problems, and plan for the future. However, before these benefits can be realized, methods and models must be updated to integrate these new data sources into existing urban and transportation planning frameworks for estimating travel demand and infrastructure usage. While recent work has made great progress extracting valid and useful measurements from new data resources, few present end-to-end solutions that transform and integrate raw, massive data into estimates of travel demand and infrastructure performance. Here we present a flexible, modular, and computationally efficient software system to fill this gap. Our system estimates multiple aspects of travel demand using call detail records (CDRs) from mobile phones in conjunction with open- and crowdsourced geospatial data, census records, and surveys. We bring together numerous existing and new algorithms to generate representative origin–destination matrices, route trips through road networks constructed using open and crowd-sourced data repositories, and perform analytics on the system’s output. We also present an online, interactive visualization platform to communicate these results to researchers, policy makers, and the public. We demonstrate the flexibility of this system by performing analyses on multiple cities around the globe. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into customary transportation demand modeling.
---
paper_title: Learning travel recommendations from user-generated GPS traces

paper_content:

The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph (TBHG). Based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like rank-by-count. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.
---
paper_title: Estimation of Statewide Origin–Destination Truck Flows From Large Streams of GPS Data: Application for Florida Statewide Model

paper_content:

This paper demonstrates the use of large streams of truck GPS data from the American Transportation Research Institute for the estimation of statewide freight truck flows in Florida. Raw GPS data streams, which comprised more than 145 million GPS records, were used to derive a database of more than 1.2 million truck trips that started or ended in Florida. The paper sheds light on the extent to which the trips derived from the GPS data captured the observed truck traffic flows in Florida. The paper includes insights into (a) the truck type composition, (b) the proportion of the observed truck traffic flows covered by the data, and (c) the geographical differences in the coverage. The paper applies origin–destination (O-D) matrix estimation to combine the GPS data with the observed truck traffic volumes at different locations within and outside Florida to derive an O-D table of truck flows within, into, and out of the state. The procedures, implementation details, and findings discussed in the paper are exp...
---
paper_title: Development of origin–destination matrices using mobile phone call data

paper_content:

Abstract In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.
---
paper_title: Building practical origin-destination (od/trip) matrices from automatically collected GPS data

paper_content:

Origin-destination matrices are key elements in transport models; however, they are costly to collect and process. Typical survey methods such as roadside interviews, or car park surveys, are expensive and disruptive. The instrument can introduce bias, by traveller avoiding the interview sites. Increasingly, authorities refuse to have live surveys on their roads due to Health and Safety concerns. Other methods for collecting origins and destinations, such as Automatic Number Plate Recognition and mailbacks to vehicle owners, suffer from privacy concerns. However, data sources exist that are routinely collected, which contain inherent information on origins and destinations. These are the locational points of GPS Tracker services, produced by vehicles fitted with advanced GPS devices and collected by organisations like TrafficMaster and iTIS. As the vehicles poll every few seconds, a huge amount of travel data is in principle available, including origins and destinations of the equipped vehicles. This data has successfully been used in the past to generate speed maps for whole regions; the increasing rate of take up of these devices means that coverage and sample sizes are now such that more detailed analyses are possible. The authors describe five applications where they have used commercially available, automatically collected GPS data to derive origin destination trip matrices. These are: 1) developing HGV origin destination matrices; 2) investigation of ODs and routing patterns for evidence against a proposed Motorway Service Area; 3) trip distribution patterns of short trips (i.e. junction hopping) and through trips using the urban motorway network; 4) roundabout turning proportions study; and, 5) roadside interviews (RSI) stability analyses between different survey years The applications give an insight into data quality and sample sizes, resulting in the ability to advise on the applicability of automatically collected GPS data for the creation of OD trip matrices. The authors are currently investigating whether and how additional travel characteristics can be determined, for example trip purpose (from land uses at origin and destination, or from the timing and regularity of the trip).
---
paper_title: Time-evolving O-D matrix estimation using high-speed GPS data streams

paper_content:

We proposed a data driven method to incrementally build O-D matrices.The Regions of Interest on this matrix are defined using spatial clustering.The sample's attributes are discretized using a novel multidimensional hierarchy.The target's p.d.f. is approximated using multidimensional histograms.This methodology is validated using 1 million of real-world trips. Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies.This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others.The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples.A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.
---
paper_title: Origin–destination trips by purpose and time of day inferred from mobile phone data

paper_content:

In this work, the authors present methods to estimate average daily origin–destination trips from triangulated mobile phone records of millions of anonymized users. These records are first converted into clustered locations at which users engage in activities for an observed duration. These locations are inferred to be home, work, or other depending on observation frequency, day of week, and time of day, and represent a user’s origins and destinations. Since the arrival time and duration at these locations reflect the observed (based on phone usage) rather than true arrival time and duration of a user, the authors probabilistically infer departure time using survey data on trips in major US cities. Trips are then constructed for each user between two consecutive observations in a day. These trips are multiplied by expansion factors based on the population of a user’s home Census Tract and divided by the number of days on which the authors observed the user, distilling average daily trips. Aggregating individuals’ daily trips by Census Tract pair, hour of the day, and trip purpose results in trip matrices that form the basis for much of the analysis and modeling that inform transportation planning and investments. The applicability of the proposed methodology is supported by validation against the temporal and spatial distributions of trips reported in local and national surveys.
---
paper_title: Urban traffic modelling and prediction using large scale taxi GPS traces

paper_content:

Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city's road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city's air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance.
---
paper_title: Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

paper_content:

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.
---
paper_title: Unveiling the complexity of human mobility by querying and mining massive trajectory data

paper_content:

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people's travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.
---
paper_title: Unravelling daily human mobility motifs

paper_content:

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.
---
paper_title: Trajectory Data Mining: An Overview

paper_content:

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
---
paper_title: Time-evolving O-D matrix estimation using high-speed GPS data streams

paper_content:

We proposed a data driven method to incrementally build O-D matrices.The Regions of Interest on this matrix are defined using spatial clustering.The sample's attributes are discretized using a novel multidimensional hierarchy.The target's p.d.f. is approximated using multidimensional histograms.This methodology is validated using 1 million of real-world trips. Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies.This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others.The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples.A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.
---
paper_title: Urban traffic modelling and prediction using large scale taxi GPS traces

paper_content:

Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city's road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city's air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance.
---
paper_title: Using Bluetooth to track mobility patterns: depicting its potential based on various case studies

paper_content:

During the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.
---
paper_title: Visually exploring movement data via similarity-based analysis

paper_content:

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data.
---
paper_title: Learning travel recommendations from user-generated GPS traces

paper_content:

The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph (TBHG). Based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like rank-by-count. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.
---
paper_title: Discovering urban activity patterns in cell phone data

paper_content:

Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.
---
paper_title: Understanding individual routing behaviour

paper_content:

Knowing how individuals move between places is fundamental to advance our understanding of human mobility (Gonzalez et al. 2008 Nature 453, 779–782. (doi:10.1038/nature06958)), improve our urban in...
---
paper_title: Collective benefits in traffic during mega events via the use of information technologies

paper_content:

Information technologies today can inform each of us about the best alternatives for shortest paths from origins to destinations, but they do not contain incentives or alternatives that manage the information efficiently to get collective benefits. To obtain such benefits, we need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way. The opportunity is that during large events the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for social good. In this paper, we integrate for the first time big data resources to quantify the impact of events and propose target strategies for collective good at urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from: mobile phones, Airbnb, Waze, and transit information, with game schedules and information of venues. Next, we evaluate the impact of the Olympic Games to the travel of commuters, and propose different route choice scenarios during the peak hours. Moreover, we gather information on the trips that contribute the most to the global congestion and that could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest path can save more collective travel time than keeping the routine routes, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented on-line for the evaluation of the public and policy makers.
---
paper_title: Discrete Choice Analysis: Theory and Application to Travel Demand

paper_content:

This book, which is intended as a graduate level text and a general professional reference, presents the methods of discrete choice analysis and their applications in the modeling of transportation systems. The first seven chapters provide a basic introduction to discrete choice analysis that covers the material needed to apply basic binary and multiple choice models. The chapters are as follows: introduction; review of the statistics of model estimation; theories of individual choice behavior; binary choice models; multinomial choice; aggregate forecasting techniques; and tests and practical issues in developing discrete choice models. The rest of the chapters cover more advanced material and culminate in the development of a complete travel demand model system presented in chapter 11. The advanced chapters are as follows: theory of sampling; aggregation and sampling of alternatives; models of multidimensional choice and the nested logit model; and systems of models. The last chapter (12) presents an overview of current research frontiers.
---
paper_title: OPTIMIZATION OF GRID TRANSIT SYSTEM IN HETEROGENEOUS URBAN ENVIRONMENT

paper_content:

Current analytic models for optimizing urban transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The model presented here shows how an optimization approach can be successful without oversimplifying the spatial characteristics and demand patterns of urban areas. This model is designed to optimize a grid transit system in a heterogeneous urban environment whose demand and supply characteristics may vary arbitrarily among adjacent zones. Network characteristics (route and station locations) and operating headways are found that minimize the total cost, including supplier and user costs. Irregular many-to-many demand patterns, zonal variations in route costs, and vehicle capacity constraints are considered in a sequential optimization process.
---
paper_title: Data-Driven Transit Network Design From Mobile Phone Trajectories

paper_content:

This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.
---
paper_title: Transit Network Design And Scheduling: a Global Review

paper_content:

This paper presents a global review of the crucial strategic and tactical steps of transit planning: the design and scheduling of the network. These steps influence directly the quality of service through coverage and directness concerns but also the economic profitability of the system since operational costs are highly dependent on the network structure. We first exhibit the context and the goals of strategic and tactical transit planning. We then establish a terminology proposal in order to name sub-problems and thereby structure the review. Then, we propose a classification of 69 approaches dealing with the design, frequencies setting, timetabling of transit lines and their combinations. We provide a descriptive analysis of each work so as to highlight their main characteristics in the frame of a two-fold classification referencing both the problem tackled and the solution method used. Finally, we expose recent context evolutions and identify some trends for future research. This paper aims to contribute to unification of the field and constitutes a useful complement to the few existing reviews.
---
paper_title: OPTIMAL TRANSIT ROUTE NETWORK DESIGN PROBLEM: ALGORITHMS, IMPLEMENTATIONS, AND NUMERICAL RESULTS

paper_content:

Previous approaches used to solve the transit route network design problem (TRNDP) can be classified into three categories: 1) Practical guidelines and ad hoc procedures; 2) Analytical optimization models for idealized situations; and 3) Meta-heuristic approaches for more practical problems. When the TRNDP is solved for a network of realistic size in which many parameters need to be determined, it is a combinatorial and NP-hard problem in nature and several sources of nonlinearities and non-convexities involved preclude guaranteed globally optimal solution algorithms. As a result, the meta-heuristic approaches, which are able to pursue reasonably good local (possibly global) optimal solutions and deal with simultaneous design of the transit route network and determination of its associated service frequencies, become necessary. The objective of this research is to systematically study the optimal TRNDP using hybrid heuristic algorithms at the distribution node level without aggregating the travel demand zones into a single node. A multi-objective nonlinear mixed integer model is formulated for the TRNDP. The proposed solution framework consists of three main components: an Initial Candidate Route Set Generation Procedure (ICRSGP) that generates all feasible routes incorporating practical bus transit industry guidelines; a Network Analysis Procedure (NAP) that determines transit trips for the TRNDP with variable demand, assigns these transit trips, determines service frequencies and computes performance measures; and a Heuristic Search Procedure (HSP) that guides the search techniques. Five heuristic algorithms, including the genetic algorithm, local search, simulated annealing, random search and tabu search, are employed as the solution methods for finding an optimal set of routes from the huge solution space. For the TRNDP with small network, the exhaustive search method is also used as a benchmark to examine the efficiency and measure the quality of the solutions obtained by using these heuristic algorithms. Several C++ program codes are developed to implement these algorithms for the TRNDP with both fixed and variable transit demand. Comprehensive experimental networks are used and successfully tested. Sensitivity analyses for each algorithm are conducted and model comparisons are performed. Numerical results are presented and the multi-objective decision making nature of the TRNDP is explored. Related characteristics underlying the TRNDP are identified, inherent tradeoffs are described and the redesign of the existing transit network is also discussed.
---
paper_title: Minimizing Transfer Times in Public Transit Network with Genetic Algorithm

paper_content:

This paper presents a systemwide approach based on a genetic algorithm for the optimization of bus transit system transfer times. The algorithm attempts to find the best feasible solution for the transfer time optimization problem by shifting existing timetables. It makes use of existing scheduled timetables and ridership data at all transfer locations and takes into consideration the randomness of bus arrivals. The complexity of the problem is mainly due to the use of a large set of binary and discrete variables. The combinatorial nature of the problem results in a significant computational burden, and thus it is difficult to solve with classical methods. Scheduling data from Broward County Transit, Florida, were used to calculate total transfer times for the existing and proposed systems. Results showed that the algorithm produced significant transfer time savings.
---
paper_title: Optimization of grid bus transit systems with elastic demand

paper_content:

Current analytic models for optimizing urban bus transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The models presented here shows how an optimization approach can be successful without oversimplifying spatial characteristics and demand patterns of urban areas and how a grid bus transit system in a heterogeneous urban environment with elastic demand is optimized. The demand distribution over the service region is discrete, which can realistically represent geographic variation. Optimal network characteristics (route and station spacings), operating headways and fare are found, which maximize the total operator profit and social welfare. Irregular service regions, many-to-many demand patterns, and vehicle capacity constraints are considered in a sequential optimization process. The numerical results show that at the optima the operator profit and social welfare functions are rather flat with respect to route spacing and headway, thus facilitating the tailoring of design variables to the actual street network and particular operating schedule without a substantial decrease in profit. The sensitivities of the design variables to some important exogenous factors are also presented.
---
paper_title: AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data

paper_content:

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens’ interactions and use data-driven insights to better plan and manage services. In this context, transit operators can leverage pervasive mobile sensing to better match observed demand for travel with their service offerings. With large scale data on mobility patterns, operators can move away from the costly and resource intensive transportation planning processes prevalent in the West, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries.
---
paper_title: Data-Driven Transit Network Design From Mobile Phone Trajectories

paper_content:

This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.
---
paper_title: Data-Driven Transit Network Design From Mobile Phone Trajectories

paper_content:

This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.
---
paper_title: Discovering Trip Patterns from Incomplete Passenger Trajectories for Inter-zonal Bus Line Planning

paper_content:

Collecting the trajectories occurring in the city and mining the patterns implied in the trajectories can support the ITS (Intelligent Transportation System) applications and foster the development of smart cities. For improving the operations of inter-zonal buses in the cities, we define a new trip pattern, i.e., frequent bus passenger trip patterns for bus lines (FBPT4BL patterns in short). We utilize the passenger trajectories from bus smart card data and propose a two-phase approach to mine FBPT4BL patterns and then recommend inter-zonal bus lines. We conduct extensive experiments on the real data from the Beijing Public Transport Group. By comparing the experimental results with the actual operation of inter-zonal buses at the Beijing Public Transport Group, we verify the validity of our proposed method.
---
paper_title: B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces

paper_content:

Taxi GPS traces can inform us the human mobility patterns in modern cities. Instead of leveraging the costly and inaccurate human surveys about people's mobility, we intend to explore the night bus route planning issue by using taxi GPS traces. Specifically, we propose a two-phase approach for bidirectional night bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick up/drop off and then propose effective methods to split big hot areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops, and bus operation time constraints, we derive several effective rules to build the bus route graph and prune invalid stops and edges iteratively. Based on this graph, we further develop a bidirectional probability-based spreading algorithm to generate candidate bus routes automatically. We finally select the best bidirectional bus route, which expects the maximum number of passengers under the given conditions and constraints. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set, which contains more than 1.57 million night passenger delivery trips, generated by 7600 taxis in a month.
---
paper_title: Performance Measures to Characterize Corridor Travel Time Delay Based on Probe Vehicle Data

paper_content:

Anonymous probe vehicle data are being collected on roadways throughout the United States. These data are incorporated into local and statewide mobility reports to measure the performance of highways and arterial systems. Predefined spatially located segments, known as traffic message channels (TMCs), are spatially and temporally joined with probe vehicle speed data. Through the analysis of these data, transportation agencies have been developing agencywide travel time performance measures. One widely accepted performance measure is travel time reliability, which is calculated along a series of TMCs. When reliable travel times are not achieved because of incidents and recurring congestion, it is desirable to understand the time and the location of these occurrences so that the corridor can be proactively managed. This research emphasizes a visually intuitive methodology that aggregates a series of TMC segments based on a cursory review of congestion hotspots within a corridor. Instead of a fixed congestio...
---
paper_title: Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency

paper_content:

Traffic flow theory has come to a point where conventional, fixed time averaged data are limiting our insight into critical behavior both at the macroscopic and microscopic scales. This paper develops a methodology to measure relationships of density and vehicle spacing on freeways. These relationships are central to most traffic flow theories but have historically been difficult to measure empirically. The work leads to macroscopic flow-density and microscopic speed-spacing relationships in the congested regime derived entirely from dual loop detector data and then verified against the NGSIM data set. The methodology eliminates the need to seek out stationary conditions and yields clean relationships that do not depend on prior assumptions of the curve shape before fitting the data. Upon review of the clean empirical relationships a key finding of this work is the fact that many of the critical parameters of the macroscopic flow-density and microscopic speed-spacing relationships depend on vehicle length, e.g., upstream moving waves should travel through long vehicles faster than through short vehicles. Thus, the commonly used assumption of a homogeneous vehicle fleet likely obscures these important phenomena. More broadly, if waves travel faster or slower depending on the length of the vehicles through which the waves pass, then the way traffic is modeled should be updated to explicitly account for inhomogeneous vehicle lengths.
---
paper_title: Quantifying Loop Detector Sensitivity and Correcting Detection Problems on Freeways

paper_content:

Loop detectors are the most commonly used vehicle detector for freeway management. A loop detector consists of a physical loop of wire embedded in the pavement connected to a sensor located in a nearby cabinet. The sensor detects the presence or absence of vehicles over the loop and typically allows a user to manually select the sensitivity level of operation to accommodate for a wide range of responsiveness from the physical loop. In conventional practice, however, it is difficult to know the physical loop’s responsiveness, which makes selecting the appropriate sensitivity level difficult. If the sensitivity and responsiveness are poorly matched it will degrade the detector’s data and the performance of applications that use the data, including: traffic management, control, and traveler information. To resolve this often overlooked problem, this paper presents an algorithm to assess how well a loop detector’s sensitivity is set by calculating the daily median on-time from the data reported by the loop detector. The algorithm can be incorporated into conventional controller software or run off-line. The result can be used both to correct the detector on-times for an inappropriate sensitivity setting in software (e.g., through a multiplicative correction factor) and to trigger an alarm to dispatch a technician to adjust the hardware sensitivity. Plotting the daily median on-time over months or years can show how the detector performance evolves. The approach is then transposed to dual-loop detectors to identify and correct for inaccurate spacing between the paired detectors. Finally, the methodology is evaluated by comparing the loop detector speeds against the concurrent velocities from a GPS-equipped probe vehicle. Although the focus of this paper is on loop detectors, with only minor modification the algorithm should also be applicable to other detector technologies that emulate loop detector operation, e.g., side-fire microwave radar.
---
paper_title: Work Zone Performance Measurement Using Probe Data

paper_content:

This report assesses the potential for probe data to support work zone performance measurement programs. It includes an overview of probe data and the advantages and disadvantages of probe data sources relative to traditional fixed sensors. It identifies when and how probe data sources can be used to support work zone performance measures. It also characterizes the applicability of different types of probe data to help manage different types of work zones. The report then exemplifies this information by presenting summaries of projects that made use of probe data for work zone performance measures or examined the capabilities and limitations of probe vehicle data. A particular focus was on a recent Maryland State Highway Administration project that provided a comprehensive example of the use of probe data sources to compute the performance measures by developing a web-based work zone performance measure application.
---
paper_title: Probe Vehicle-Based Statewide Mobility Performance Measures for Decision Makers

paper_content:

Decision makers in state transportation agencies typically manage budgets approaching or exceeding $1 billion. Historically, the data used to make investment decisions have been quite coarse and have been typically based on short-term volume counts fed into models to forecast performance. As a result, it is not uncommon for construction projects to address needs that were forecast to be a priority 5 to 10 years earlier, while more pressing congestion challenges go unmet. It is essential that long-term planning begins to be supplemented by more current performance measures. The emerging private-sector probe vehicle data obtained from commercial providers offer an opportunity to augment traditional forward-looking planning models with performance measures that reflect the conditions motorists are experiencing today. This paper proposes scalable, analytical probe data-reduction techniques to create technically sound, yet visually intuitive, system-performance measures of current freeway conditions. These typ...
---
paper_title: Algorithm for Detector-Error Screening on Basis of Temporal and Spatial Information

paper_content:

Although average effective vehicle length (AEVL) has been recognized as one of the most popular methods for detecting data errors, how to set proper thresholds so as to prevent false alarms and missed detections remains a challenging ongoing issue. This study proposed a sequential screening algorithm that employed multiple comparisons with the best statistics to compare concurrently the estimated AEVLs between lanes and stations for assessment of the data quality of a target detector. With both the temporal and spatial information, the proposed method can reliably generate a confidence interval and determine whether the target detector is malfunctioning or in need of calibration. The proposed algorithm was tested with 2 weeks of detector data from Ocean City, Maryland. The analysis results demonstrate the effectiveness of the proposed sequential screening algorithm and its potential for field applications.
---
paper_title: Comparing INRIX speed data against concurrent loop detector stations over several months

paper_content:

Abstract Many real-time traffic-monitoring applications only require speed or travel time. In recent years INRIX Traffic has started collecting and selling real-time speed data collected from “a variety of sources.” The clients include direct to consumer and operating agencies alike. So far the INRIX speed data have received little independent evaluation in the literature, with only a few published studies. The current study exploits a unique juncture as the Ohio Department of Transportation transitioned from loop detectors to third party traffic data for real time management. The two traffic surveillance systems operated concurrently for about half a year in Columbus, Ohio, USA. This paper uses two months of the concurrent data to evaluate INRIX performance on 14 mi of I-71, including both recurrent and non-recurrent events. The work compared reported speeds from INRIX against the concurrent loop detector data, as detailed herein. Three issues became apparent: First, the reported INRIX speeds tend to lag the loop detector measurements by almost 6 min. This latency appears to be within INRIX specifications, but from an operational standpoint it is important that time sensitive applications account for it, e.g., traffic responsive ramp metering. Second, although INRIX reports speed every minute, most of the time the reported speed is identical to the previous sample, suggesting that INRIX is effectively calculating the speeds over a longer period than it uses to report the speeds. This work observed an effective average sampling period of 3–5 min, with many periods of repeated reported speed lasting in excess of 10 min. Third, although INRIX reports two measures of confidence, these confidence measures do not appear to reflect the latency or the occurrence of repeated INRIX reported speeds.
---
paper_title: Traffic Flow Dynamics: Data, Models and Simulation

paper_content:

This instructional guide describes the use of simulation and mathematical models in determining traffic flow dynamics. Part 1 focuses on data collection with chapters on trajectory and floating-car data, cross-sectional data, and spatiotemporal reconstruction of the traffic state. Part 2 presents various mathematical models including: continuity equations, the Lighthill–Whitham–Richards Model, macroscopic models, car-following models, lane-changing models, stability analysis, and phase diagrams. Part 3 outlines traffic flow theory applications. Chapters include: travel time estimation, fuel consumption and emissions, and optimization. This textbook includes problems and solutions and an accompanying website, www.traffic-flow-dynamics.org, provides some interactive content.
---
paper_title: Data Assimilation: The Ensemble Kalman Filter

paper_content:

Statistical definitions.- Analysis scheme.- Sequential data assimilation.- Variational inverse problems.- Nonlinear variational inverse problems.- Probabilistic formulation.- Generalized Inverse.- Ensemble methods.- Statistical optimization.- Sampling strategies for the EnKF.- Model errors.- Square Root Analysis schemes.- Rank issues.- Spurious correlations, localization, and inflation.- An ocean prediction system.- Estimation in an oil reservoir simulator.
---
paper_title: Calibration Framework based on Bluetooth Sensors for Traffic State Estimation Using a Velocity based Cell Transmission Model

paper_content:

The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.
---
paper_title: Travel time prediction using the GPS test vehicle and Kalman filtering techniques

paper_content:

A sudden traffic surge immediately after special events (e.g., conventions, concerts) can create substantial traffic congestion in the area where the events are held. It is desired that the special events related traffic performance can be measured so that the traffic flow can be improved via some existing methods such as a temporary traffic signal timing adjustment. This paper focuses on the study of the arterial travel time prediction using the Kalman filtering and estimation technique, and a graduation ceremony is chosen as our case study. The Global Positioning System (GPS) test vehicle technique is used to collect after events travel time data. Based on the real-time data collected, a discrete-time Kalman filter is then applied to predict travel time exiting the area under study. An assessment of the performance and its effectiveness at the test site are investigated. The approaches to further improve the accuracy of the prediction error are also discussed.
---
paper_title: Kalman Filter Approach to Speed Estimation Using Single Loop Detector Measurements under Congested Conditions

paper_content:

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional g-factor approach.
---
paper_title: A STATISTICAL ALGORITHM FOR ESTIMATING SPEED FROM SINGLE LOOP VOLUME AND OCCUPANCY MEASUREMENTS

paper_content:

This paper presents an algorithm for estimating mean traffic speed using volume and occupancy data from a single inductance loop. The algorithm is based on the statistics of the measurements obtained from a traffic management system. The algorithm produces an estimate of speed and provides a reliability test for the speed estimate.
---
paper_title: Trajectory Data Mining: An Overview

paper_content:

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
---
paper_title: Assessing Expected Accuracy of Probe Vehicle Travel Time Reports

paper_content:

The use of probe vehicles to provide estimates of link travel times has been suggested as a means of obtaining travel times within signalized networks for use in advanced travel information systems. Past research in the literature has provided contradictory conclusions regarding the expected accuracy of these probe-based estimates, and consequently has estimated different levels of market penetration of probe vehicles required to sustain accurate data within an advanced traveler information system. This paper examines the effect of sampling bias on the accuracy of the probe estimates. An analytical expression is derived on the basis of queuing theory to prove that bias in arrival time distributions and/or in the proportion of probes associated with each link departure turning movement will lead to a systematic bias in the sample estimate of the mean delay. Subsequently, the potential for and impact of sampling bias on a signalized link is examined by simulating an arterial corridor. The analytical derivation and the simulation analysis show that the reliability of probe-based average link travel times is highly affected by sampling bias. Furthermore, this analysis shows that the contradictory conclusions of previous research are directly related to the presence or absence of sample bias.
---
paper_title: OPTIMIZATION OF GRID TRANSIT SYSTEM IN HETEROGENEOUS URBAN ENVIRONMENT

paper_content:

Current analytic models for optimizing urban transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The model presented here shows how an optimization approach can be successful without oversimplifying the spatial characteristics and demand patterns of urban areas. This model is designed to optimize a grid transit system in a heterogeneous urban environment whose demand and supply characteristics may vary arbitrarily among adjacent zones. Network characteristics (route and station locations) and operating headways are found that minimize the total cost, including supplier and user costs. Irregular many-to-many demand patterns, zonal variations in route costs, and vehicle capacity constraints are considered in a sequential optimization process.
---
paper_title: Instantaneous Emission Modeling with GPS-Based Vehicle Activity Data: Results of Diesel Trucks for One-Day Trips

paper_content:

This paper presents an instantaneous analysis for traffic emissions using GPS-based vehicle activity data. The different driving conditions, including real-time and average speed, short-time stops and long-time stops, acceleration and deceleration, etc., are extracted from GPS data. The hot emission, cold-start emission and idling emission, varied by nitrogen compounds and particulate matter are calculated, respectively, in terms of the driving condition and vehicle characteristics. Results simulated based on a one-day trip activity dataset show that trucks spend most kilometers on national roads, followed by municipal and provincial roads. The number of short-time stops is significantly higher than long-time stops, and the time spent for long-time stops is higher than short-time duration. The hot emission accounts for the largest proportion of emissions, and the idling emission also contribute substantially. Results of sensitivity analyses indicate that pollutions in urban area from freight transport can be significantly decreased by increasing the vehicle classes and guiding the heavy trucks out of the region.
---
paper_title: The effects of route choice decisions on vehicle energy consumption and emissions

paper_content:

Motorists typically select routes that minimize their travel time or generalized cost. This may entail traveling on longer but faster routes. This raises questions concerning whether traveling along a longer but faster route results in energy and/or air quality improvements. We investigate the impacts of route choice decisions on vehicle energy consumption and emission rates for different vehicle types using microscopic and macroscopic emission estimation tools. The results demonstrate that the faster highway route choice is not always the best from an environmental and energy consumption perspective. Specifically, significant improvements to energy and air quality can be achieved when motorists utilize a slower arterial route although they incur additional travel time. The study also demonstrates that macroscopic emission estimation tools (e.g., MOBILE6) can produce erroneous conclusions given that they ignore transient vehicle behavior along a route. The findings suggest that an emission- and energy-optimized traffic assignment can significantly improve emissions over the standard user equilibrium and system optimum assignment formulations. Finally, the study demonstrates that a small portion of the entire trip involves high engine-load conditions that produce significant increases in emissions; demonstrating that by minimizing high-emitting driving behavior, air quality can be improved significantly.
---
paper_title: DEVELOPMENT OF VT-MICRO MODEL FOR ESTIMATING HOT STABILIZED LIGHT DUTY VEHICLE AND TRUCK EMISSIONS

paper_content:

Abstract The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.
---
paper_title: Statistical Vehicle Specific Power Profiling for Urban Freeways

paper_content:

Vehicle Specific Power (VSP) is conventionally defined to represent the instantaneous vehicle engine power. It has been widely utilized to reveal the impact of vehicle operating conditions on emission and energy consumption estimates that are dependent upon speed, roadway grade and acceleration or deceleration on the basis of the second-by-second vehicle operation. VSP has hence been incorporated into a key contributing factor in the vehicle emission models including Motor Vehicle Emission Simulator (MOVES). To facilitate the preparation of MOVES vehicle operating mode distribution inputs, an enhanced understanding and modeling of VSP distribution versus roadway grade become indispensable. This paper presents a study in which previous studies are extended by deeply investigating the characteristics of VSP distributions and their impacts due to varying freeway grades, as well as time-of-day factors. Afterwards, statistical distribution models with a scope of bins is identified through a goodness of fit testing approach by using the sample data collected from the interstate freeway segments in Cincinnati area. The Global Positioning System (GPS) data were collected at a selected length of 30 km urban freeway for AM, PM and Mid-day periods. The datasets representing the vehicle operating conditions for VSP calculation are then extracted from the GPS trajectory data. The distribution fit modeling results demonstrated that the Wakeby distribution with five parameters dominates the most fitting parameters with the samples. In addition, the speed variation lies behind the time of day differences is also identified to be a contributing factor of urban freeway VSP distribution.
---
paper_title: Model-based traffic control for balanced reduction of fuel consumption , emissions , and travel time ∗

paper_content:

Abstract In this paper we integrate the macroscopic traffic flow model METANET with the microscopic dynamic emission and fuel consumption model VT-Micro. We use the integrated models in the model predictive control (MPC) framework to reduce exhaust emissions, fuel consumption, and travel time using dynamic speed limit control. With simulation experiments we demonstrate the countereffects and conflicting nature of the different traffic control objectives. Our simulation results indicate that a model-based traffic control approach, particularly MPC, can be used to obtain a balanced trade-off between the conflicting traffic control objectives.
---
paper_title: How Much Does Traffic Congestion Increase Fuel Consumption and Emissions? Applying Fuel Consumption Model to NGSIM Trajectory Data

paper_content:

The fuel consumption of vehicular traffic (and associated CO2 emissions) on a given road section depends strongly on the velocity profiles of the vehicles. The basis for a detailed estimation is therefore the consumption rate as a function of instantaneous velocity and acceleration. This paper will present a model for the instantaneous fuel consumption that includes vehicle properties, engine properties, and gear-selection schemes and implement it for different passenger car types representing the vehicle fleet under consideration. The paper will apply the model to trajectories from microscopic traffic simulation. The proposed model can directly be used in a microscopic traffic simulation software to calculate fuel consumption and derived emission such as carbon dioxide. Next to travel times, the fuel consumption is an important measure for the performance of future Intelligent Transportation Systems. Furthermore, the model is applied to real traffic situations by taking the velocity and acceleration as input from several sets of the NGSIM trajectory data. Dedicated data processing and smoothing algorithms have been applied to the NGSIM data to suppress the data noise that is multiplied by the necessary differentiations for obtaining more realistic velocity and acceleration time series. On the road sections covered by the NGSIM data, we found that traffic congestion typically lead to an increase of fuel consumption of the order of 80% while the traveling time has increased by a factor of up to 4. We conclude that the influence of congestion on fuel consumption is distinctly lower than that on travel time.
---
paper_title: A data-driven optimization-based approach for siting and sizing of electric taxi charging stations

paper_content:

Abstract This paper presents a data-driven optimization-based approach to allocate chargers for battery electric vehicle (BEV) taxis throughout a city with the objective of minimizing the infrastructure investment. To account for charging congestion, an M / M / x / s queueing model is adopted to estimate the probability of BEV taxis being charged at their dwell places. By means of regression and logarithmic transformation, the charger allocation problem is formulated as an integer linear program (ILP), which can be solved efficiently using Gurobi solver. The proposed method is applied using large-scale GPS trajectory data collected from the taxi fleet of Changsha, China. The key findings from the results include the following: (1) the dwell pattern of the taxi fleet determines the siting of charging stations; (2) by providing waiting spots, in addition to charging spots, the utilization of chargers increases and the number of required chargers at each site decreases; and (3) the tradeoff between installing more chargers versus providing more waiting spaces can be quantified by the cost ratio of chargers and parking spots.
---
paper_title: Learning transportation mode from raw gps data for geographic applications on the web

paper_content:

Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-based segmentation method, an inference model and a post-processing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.
---
paper_title: Optimization of Charging Infrastructure for Electric Taxis

paper_content:

For the transportation sector, electromobility presents a chance both to abolish oil dependency and to open the possibility of using sustainable energy sources. In the case of taxis, the substitution of electric vehicles for conventional vehicles with an internal combustion engine may be especially favorable because the driving patterns involve several waiting periods, which can be used for recharging the battery. An infrastructure consisting of charging stations at taxi stands needs to be developed to ensure the energy supply of these taxis. Designing a charging infrastructure requires the development of an optimization method to maximize the economic benefit of the whole system, consisting of electric taxis (e-taxis) and charging stations (CSs). The number of charging stations should be kept as minimal as possible to reduce costs. Simultaneously, the energy supply for these taxis has to be ensured to enable high mileage and earnings. This study introduces an event-based simulation of the e-taxis' mileag...
---
paper_title: Transportation mode detection using mobile phones and GIS information

paper_content:

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naive Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.
---
paper_title: Where do cyclists ride? A route choice model developed with revealed preference GPS data

paper_content:

To better understand bicyclists’ preferences for facility types, GPS units were used to observe the behavior of 164 cyclists in Portland, Oregon, USA for several days each. Trip purpose and several other trip-level variables recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. The authors used the 1449 non-exercise, utilitarian trips to estimate a bicycle route choice model. The model used a choice set generation algorithm based on multiple permutations of path attributes and was formulated to account for overlapping route alternatives. The findings suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control (e.g. presence or absence of traffic signals), and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways with traffic calming features (aka “bicycle boulevards”), and bridge facilities. Bike lanes more or less exactly offset the negative effects of adjacent traffic, but were no more or less attractive than a basic low traffic volume street. Finally, route preferences differ between commute and other utilitarian trips; cyclists were more sensitive to distance and less sensitive to other infrastructure characteristics for commute trips.
---
paper_title: Mapping cyclist activity and injury risk in a network combining smartphone GPS data and bicycle counts.

paper_content:

In recent years, the modal share of cycling has been growing in North American cities. With the increase of cycling, the need of bicycle infrastructure and road safety concerns have also raised. Bicycle flows are an essential component in safety analysis. The main objective of this work is to propose a methodology to estimate and map bicycle volumes and cyclist injury risk throughout the entire network of road segments and intersections on the island of Montreal, achieved by combining smartphone GPS traces and count data. In recent years, methods have been proposed to estimate average annual daily bicycle (AADB) volume and injury risk estimates at both the intersection and segment levels using bicycle counts. However, these works have been limited to small samples of locations for which count data is available. In this work, a methodology is proposed to combine short- and long-term bicycle counts with GPS data to estimate AADB volumes along segments and intersections in the entire network. As part of the validation process, correlation is observed between AADB values obtained from GPS data and AADB values from count data, with R-squared values of 0.7 for signalized intersections, 0.58 for non-signalized intersections and between 0.48 and 0.76 for segments with and without bicycle infrastructure. The methodology is also validated through the calibration of safety performance functions using both sources of AADB estimates, from counts and from GPS data. Using the validated AADB estimates, the factors associated with injury risk were identified using data from the entire population of intersections and segments throughout Montreal. Bayesian injury risk maps are then generated and the concentrations of expected injuries and risk at signalized intersections are identified. Signalized intersections, which are often located at the intersection of major arterials, witness 4 times more injuries and 2.5 times greater risk than non-signalized intersections. A similar observation can be made for arterials which not only have a higher concentration of injuries but also injury rates (risk). On average, streets with cycle tracks have a greater concentration of injuries due to greater bicycle volumes, however, and in accordance with recent works, the individual risk per cyclist is lower, justifying the benefits of cycle tracks.
---
paper_title: Understanding the Impact of Electric Vehicle Driving Experience on Range Anxiety

paper_content:

Objective: The objective of the present research was to increase understanding of the phenomenon of range anxiety and to determine the degree to which practical experience with battery electric vehicles (BEVs) reduces different levels of range anxiety. Background: Limited range is a challenge for BEV users. A frequently discussed phenomenon in this context is range anxiety. There is some evidence suggesting that range anxiety might only be a problem for inexperienced BEV drivers, and therefore, might decrease with practical experience. Method: We compared 12 motorists with high BEV driving experience (M = 60.500 km) with 12 motorists, who had never driven a BEV before. The test drive was designed to lead to a critical range situation (remaining range < trip length). We examined range appraisal and range stress (i.e., range anxiety) on different levels (cognitive, emotional and behavioral). Results: Experienced BEV drivers exhibited less negative range appraisal and range anxiety than inexperienced BEV drivers, revealing significant, strong effects for all but one variable. Conclusion: Hence, BEV driving experience (defined as absolute km driven with a BEV) seems to be one important variable that predicts less range anxiety. Application: In order to reduce range anxiety in BEV drivers even when there is a critical range situation, it is important to increase efficiency and effectiveness of the learning process.
---
paper_title: Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

paper_content:

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.
---
paper_title: Using Bluetooth to track mobility patterns: depicting its potential based on various case studies

paper_content:

During the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.
---
paper_title: Health and environmental benefits related to electric vehicle introduction in EU countries

paper_content:

Abstract Introduction of electric vehicles (EV) can help to reduce CO2-emissions and the dependence on petroleum products. However, sometimes relatively larger air pollutant emissions from certain power plants can offset the benefits of replacing internal combustion engine (ICE) cars with EV. The goal of this study was to compare the societal impact (climate change & health effects) of EV introduction in the EU-27 under different scenarios for electricity production. The analysis shows that countries that rely on low air pollutant emitting fuel mixes may gain millions of Euro/annum in terms of avoided external costs. Benefits extend across the EU, especially for emissions in small countries. Transport pollution affects the local scale, while electricity pollution has a regional reach. Other European countries, that depend on more polluting fuel mixes, may not benefit at all from introducing EV. Data on the present fuel mix were available for Belgium, France, Portugal, Denmark and the UK on a detailed time scale (5–30′ basis) and show that the time dependent variation of external cost for charging EV is dwarfed compared to the overall gain for introducing EV. The largest benefit is found in not driving an ICE car and avoiding local combustion related emissions. Data on the present fuel mix were also available for Romania on a detailed time scale (10′) and show that the variation in external costs is relatively larger than for the other countries and at some moments it may be worth the effort, at least in theory, to reschedule EV loading schemes taking into account social impact analysis.
---
paper_title: Understanding mobility based on GPS data

paper_content:

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.
---
paper_title: Sensing Human Activity: GPS Tracking

paper_content:

The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools, but also as instruments used to capture travelled routes: as sensors that measure activity on a city scale or the regional scale. TU Delft developed a process and database architecture for collecting data on pedestrian movement in three European city centres, Norwich, Rouen and Koblenz, and in another experiment for collecting activity data of 13 families in Almere (The Netherlands) for one week. The question posed in this paper is: what is the value of GPS as ‘sensor technology’ measuring activities of people? The conclusion is that GPS offers a widely useable instrument to collect invaluable spatial-temporal data on different scales and in different settings adding new layers of knowledge to urban studies, but the use of GPS-technology and deployment of GPS-devices still offers significant challenges for future research.
---
paper_title: Bicycle Route Choice Data Collection using GPS-Enabled Smartphones

paper_content:

The proliferation of consumer-grade smartphones with global position satellite (GPS) location capabilities opens a new data collection method for researchers. When confronted with a lack of data on bicycle routes preferred by local cyclists, the San Francisco County Transportation Authority (SFCTA) developed a freely downloadable iPhone/Android smartphone “app” called CycleTracks to collect actual bicycle routes traversed by city cyclists. Cooperation with local bicycle advocacy groups, along with social media and email campaigns, encouraged use of the app by regular citizenry. Several rounds of pre-release testing showed that making the app start up quickly, and minimizing battery usage during recording, were critical to getting good data. Once installed on a user's smartphone, a single “tap” would start and stop recording a bicycle trip; after completing a trip, the app automatically uploaded the track to a central database/web server, via the phone's built-in data plan. Approximately 5,000 usable bicycle trips were collected from hundreds of users in the region. Demographic data was optionally provided by some users, and showed a bias toward frequent cyclists, and toward male users (even more than cycling is already male-dominated in the region's most recent household travel survey). A bicycle route choice model developed using the data revealed sensitivity to slope, presence of bike lanes and/or bike route designations, trip purpose, and gender. The bike route choice model is now being integrated into San Francisco's regional travel model.
---
paper_title: Safety route guidance system using participatory sensing

paper_content:

This study proposes a safety route guidance system used after a natural disaster by gathering sensing data from pedestrians' smartphones. In order to find the problems caused by a disaster such as collapsed house, fissures and floods, the system uses the information retrieved from pedestrians' smartphones with GPS receivers and accelerometers. In addition, the system does not use the information from the map data before the disaster for detecting the geographical information in case of large-scale disasters. Therefore, the map data after a disaster is mapped with only the information after the disaster. As a result, pedestrians' moving paths and walking states are evaluated and used for evacuation guidance.
---
paper_title: An Evacuation Route Planning for Safety Route Guidance System after Natural Disaster Using Multi-objective Genetic Algorithm

paper_content:

When a natural disaster occurred, some roads cannot be used anymore and sometimes blocked. Also, survivors and refugees cannot follow the evacuation procedures by just using default maps after disaster. A previous study proposed a safety route guidance system that can be used after natural disasters by using participatory sensing. The system estimates safe routes and generates an evacuation map by collecting GPS data and accelerometer data from pedestrians' smartphone. However, the system does not base on default map data. After that, the system evaluates the safety of each route. However, the previous study did not propose a method of finding evacuation routes from the users' current location to their destination. Therefore, in this study, we proposed a method of evacuation route planning. We have implemented Multi-Objective Genetic Algorithm (MOGA) into the route planning methodology. The proposed system has three objective functions, which are: evacuation distance, evacuation time and safety of evacuation route. Also, we proposed a new safety evaluation method. As a result, this study gives a better reflection of the change of road conditions. Also, the safety evaluation values are more useful than the previous study's evaluation method of the route. Moreover, the system can provide evacuation routes with different characteristics to users. As a result, the users can select a route which is suitable for their situation.
---
paper_title: Intelligent system for urban emergency management during large-scale disaster

paper_content:

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, urban emergency management has become the especially important issue for the whole governments around the world. In this paper, we present a novel intelligent system for urban emergency management during the large-scale disasters. The proposed system stores and manages the global positioning system (GPS) records from mobile devices used by approximately 1.6 million people throughout Japan over one year. By mining and analyzing population movements after the Great East Japan Earthquake, our system can automatically learn a probabilistic model to better understand and simulate human mobility during the emergency situations. Based on the learning model, population mobility in various urban areas impacted by the earthquake throughout Japan can be automatically simulated or predicted. On the basis of such kind of system, it is easy for us to find some new features or population mobility patterns after the recent and unprecedented composite disasters, which are likely to provide valuable experience and play a vital role for future disaster management worldwide.
---
paper_title: Traffic Monitoring immediately after a major natural disaster as revealed by probe data – A case in Ishinomaki after the Great East Japan Earthquake

paper_content:

This study analyzes how people behaved and traffic congestion expanded immediately after the Great East Japan Earthquake on March 11, 2011 using information such as probe vehicle and smartphone GPS data. One of the cities most seriously damaged during the earthquake was Ishinomaki. Understanding human evacuation behavior and observing road network conditions are key for the creation of effective evacuation support plans and operations. In many cases, however, a major natural disaster destroys most infrastructure sensors and detailed dynamic information on people’s movements cannot be recorded. Following the Great East Japan Earthquake, vehicle detectors did not work due to the severe tsunami and electric power failure. Therefore, information was only available from individuals’ probe vehicles and smartphone GPS data. These probe data, along with disaster measurements such as water immersion levels, revealed the sudden transition of vehicle speed (i.e., it eventually slowed to less than walking speed and a serious gridlock phenomenon in the Ishinomaki central area occurred). These quantitative findings, which could not be identified without probe data, should be utilized during future disaster mitigation planning.
---
paper_title: Introducing naturalistic cycling data: what factors influence bicyclists' safety in the real world?

paper_content:

Presently, the collection and analysis of naturalistic data is the most credited method for understanding road user behavior and improving traffic safety. Such methodology was developed for motorized vehicles, such as cars and trucks, and is still largely applied to those vehicles. However, a reasonable question is whether bicycle safety can also benefit from the naturalistic methodology, once collection and analyses are properly ported from motorized vehicles to bicycles. This paper answers this question by showing that instrumented bicycles can also collect analogous naturalistic data. In addition, this paper shows how naturalistic cycling data from 16 bicyclists can be used to estimate risk while cycling. The results show that cycling near an intersection increased the risk of experiencing a critical event by four times, and by twelve times when the intersection presented some form of visual occlusion (e.g., buildings and hedges). Poor maintenance of the road increased the risk tenfold. Furthermore, the risk of experiencing a critical event was twice as large when at least one pedestrian or another bicyclist crossed the bicyclist’s trajectory. Finally, this study suggests the two most common scenarios for bicycle accidents, which result from different situations and thus require different countermeasures. The findings presented in this paper show that bicycle safety can benefit from the naturalistic methodology, which provides data able to guide development and evaluation of (intelligent) countermeasures to increase cycling safety.
---
paper_title: Categorizing bicycling environments using GPS-based public bicycle speed data

paper_content:

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.
---
paper_title: Mapping cyclist activity and injury risk in a network combining smartphone GPS data and bicycle counts.

paper_content:

In recent years, the modal share of cycling has been growing in North American cities. With the increase of cycling, the need of bicycle infrastructure and road safety concerns have also raised. Bicycle flows are an essential component in safety analysis. The main objective of this work is to propose a methodology to estimate and map bicycle volumes and cyclist injury risk throughout the entire network of road segments and intersections on the island of Montreal, achieved by combining smartphone GPS traces and count data. In recent years, methods have been proposed to estimate average annual daily bicycle (AADB) volume and injury risk estimates at both the intersection and segment levels using bicycle counts. However, these works have been limited to small samples of locations for which count data is available. In this work, a methodology is proposed to combine short- and long-term bicycle counts with GPS data to estimate AADB volumes along segments and intersections in the entire network. As part of the validation process, correlation is observed between AADB values obtained from GPS data and AADB values from count data, with R-squared values of 0.7 for signalized intersections, 0.58 for non-signalized intersections and between 0.48 and 0.76 for segments with and without bicycle infrastructure. The methodology is also validated through the calibration of safety performance functions using both sources of AADB estimates, from counts and from GPS data. Using the validated AADB estimates, the factors associated with injury risk were identified using data from the entire population of intersections and segments throughout Montreal. Bayesian injury risk maps are then generated and the concentrations of expected injuries and risk at signalized intersections are identified. Signalized intersections, which are often located at the intersection of major arterials, witness 4 times more injuries and 2.5 times greater risk than non-signalized intersections. A similar observation can be made for arterials which not only have a higher concentration of injuries but also injury rates (risk). On average, streets with cycle tracks have a greater concentration of injuries due to greater bicycle volumes, however, and in accordance with recent works, the individual risk per cyclist is lower, justifying the benefits of cycle tracks.
---
paper_title: Analysis of cyclist behavior using naturalistic data: data processing for model development

paper_content:

Cycling has been increasingly popular in many cities over the past decades because of its benefits for both environment and human health. However, there is still lack of knowledge on the characteristics specific to this traveler group and recent promotion of bicycle use in transport policies has even expanded the demand for understanding cyclist behavior and bicycle dynamics. It is believed that such understanding can further facilitate the evaluation and improvement of cycling safety as well as accessibility on the network. This paper therefore presents an essential methodological framework for processing and analyzing naturalistic data collected by commuter cyclists in Stockholm equipped with portable GPS devices. On one hand, the GPS coordinates are filtered by the Kalman smoothing algorithm to obtain accurate and consistent estimates of cyclistsr position, speed and acceleration. On the other locally weighted regression is applied to abstract gradient profiles of cycling paths using data of both altitude and travel distance. After information estimation, the characteristics of cyclist acceleration behavior are then analyzed using statistical approaches. The results show that the acceleration profiles have a linear correlation with the total variance in speed during acceleration or deceleration. The data is finally applied to identify cyclist acceleration models proposed for the development of cycling simulation.
---
paper_title: Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders.

paper_content:

As electric bicycles (e-bikes) have emerged as a new transportation mode, their role in transportation systems and their impact on users have become important issues for policy makers and engineers. Little safety-related research has been conducted in North America or Europe because of their relatively small numbers. This work describes the results of a naturalistic GPS-based safety study between regular bicycle (i.e., standard bicycle) and e-bike riders in the context of a unique bikesharing system that allows comparisons between instrumented bike technologies. We focus on rider safety behavior under four situations: (1) riding in the correct direction on directional roadway segments, (2) speed on on-road and shared use paths, (3) stopping behavior at stop-controlled intersections, and (4) stopping behavior at signalized intersections. We find that, with few exceptions, riders of e-bike behave very similarly to riders of bicycles. Violation rates were very high for both vehicles. Riders of regular bicycles and e-bikes both ride wrong-way on 45% and 44% of segments, respectively. We find that average on-road speeds of e-bike riders (13.3kph) were higher than regular bicyclists (10.4kph) but shared use path (greenway) speeds of e-bike riders (11.0kph) were lower than regular bicyclists (12.6kph); both significantly different at >95% confidence. At stop control intersections, both bicycle and e-bike riders violate the stop signs at the similar rate with bicycles violating stop signs at a slightly higher rate at low speed thresholds (∼80% violations at 6kph, 40% violations at 11kph). Bicycles and e-bikes violate traffic signals at similar rates (70% violation rate). These findings suggest that, among the same population of users, e-bike riders exhibit nearly identical safety behavior as regular bike riders and should be regulated in similar ways. Users of both technologies have very high violation rates of traffic control devices and interventions should occur to improve compliance.
---
paper_title: Modeling work zone crash frequency by quantifying measurement errors in work zone length.

paper_content:

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.
---
paper_title: Predicting Motor Vehicle Crashes Using Support Vector Machine Models

paper_content:

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes.
---
paper_title: A GPS APPROACH FOR THE ANALYSIS OF CAR FOLLOWING BEHAVIOR

paper_content:

The objective of this research study was to develop a GPS methodology for the collection of car following data under actual highway driving conditions. The methodology involved the application of GPS hardware and software to simultaneously collect speed and location information for two test vehicles. Vehicle position information was then linearly referenced using a GIS. The data were analyzed using both numerical and graphical methods to examine the relationship between the relative positions, speeds, and accelerations of the vehicles. Based on this research a simplified, cost effective methodology for the collection reduction, and analysis of car following behavior is presented
---
paper_title: Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams

paper_content:

This paper proposes a clustering approach to predict the probability of a collision occurring in the proximity of planned road maintenance operations (i.e., work zones). The proposed method is applied to over 54,000 short-term work zones in the state of Maryland and demonstrates an ability to predict work zone collision probabilities. One of the key applications of this work is using the predicted probabilities at the operational level to help allocate highway response teams. To this end, a two-stage stochastic program is used to locate response vehicles on the Maryland highway network in order to minimize expected response times.
---
paper_title: Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

paper_content:

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.
---
paper_title: Work zone safety analysis and modeling: a state-of-the-art review.

paper_content:

OBJECTIVE ::: Work zone safety is one of the top priorities for transportation agencies. In recent years, a considerable volume of research has sought to determine work zone crash characteristics and causal factors. Unlike other non-work zone-related safety studies (on both crash frequency and severity), there has not yet been a comprehensive review and assessment of methodological approaches for work zone safety. To address this deficit, this article aims to provide a comprehensive review of the existing extensive research efforts focused on work zone crash-related analysis and modeling, in the hopes of providing researchers and practitioners with a complete overview. ::: ::: ::: METHODS ::: Relevant literature published in the last 5 decades was retrieved from the National Work Zone Crash Information Clearinghouse and the Transport Research International Documentation database and other public digital libraries and search engines. Both peer-reviewed publications and research reports were obtained. Each study was carefully reviewed, and those that focused on either work zone crash data analysis or work zone safety modeling were identified. The most relevant studies are specifically examined and discussed in the article. ::: ::: ::: RESULTS ::: The identified studies were carefully synthesized to understand the state of knowledge on work zone safety. Agreement and inconsistency regarding the characteristics of the work zone crashes discussed in the descriptive studies were summarized. Progress and issues about the current practices on work zone crash frequency and severity modeling are also explored and discussed. The challenges facing work zone safety research are then presented. ::: ::: ::: CONCLUSIONS ::: The synthesis of the literature suggests that the presence of a work zone is likely to increase the crash rate. Crashes are not uniformly distributed within work zones and rear-end crashes are the most prevalent type of crashes in work zones. There was no across-the-board agreement among numerous papers reviewed on the relationship between work zone crashes and other factors such as time, weather, victim severity, traffic control devices, and facility types. Moreover, both work zone crash frequency and severity models still rely on relatively simple modeling techniques and approaches. In addition, work zone data limitations have caused a number of challenges in analyzing and modeling work zone safety. Additional efforts on data collection, developing a systematic data analysis framework, and using more advanced modeling approaches are suggested as future research tasks.
---
paper_title: Safety Models for Rural Freeway Work Zones

paper_content:

Construction and maintenance work zones have traditionally been hazardous locations within the highway environment. Studies show that the accident rates during road construction are generally higher than during periods of regular traffic operations. The increase in the number of crashes may be attributed to (a) general disruption to the flowing traffic due to sudden discontinuities caused by closed lanes, (b) improper lane merging maneuvers, (c) the presence of heavy construction equipment within the work area, (d) inappropriate use of traffic control devices, and (e) poor traffic management. Research was conducted to develop regression models predicting the expected number of crashes at work zones on rural, two-lane freeway segments. Crashes on approaches to work zones and those inside the work zones were analyzed separately. For developing these models, an extensive database was obtained, including freeway data, crash data, and work zone characteristics. Negative binomial models were developed with aver...
---
paper_title: Analysis of Crash Frequency in Work Zones with Focus on Police Enforcement

paper_content:

Highway work zone safety has been a concern nationwide and will likely draw ever increasing attention as more highway funds are invested in the maintenance of existing highways. To improve work zone safety, the Indiana Department of Transportation (DOT) established a special fund for work zone patrolling, and this study was commissioned to help the Indiana DOT achieve the maximum safety benefits within its budget constraint. With help from the Indiana DOT, a survey of project engineers was conducted to collect work zone information. The findings from the survey were linked with other available data. A random-effect negative binomial model was developed to identify the contributing factors and to estimate crash frequency in highway work zones. The results from the model provided insight for better understanding of crashes in work zones. Various factors, including roadway information, traffic volume, work zone-specific features, and police presence, were identified as things that affect crash frequency in w...
---
paper_title: OpenStreetMap: User-Generated Street Maps

paper_content:

The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.
---
paper_title: Hidden Markov map matching through noise and sparseness

paper_content:

The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.
---
paper_title: Visually exploring movement data via similarity-based analysis

paper_content:

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data.
---
paper_title: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

paper_content:

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.
---
paper_title: Using Bluetooth to track mobility patterns: depicting its potential based on various case studies

paper_content:

During the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.
---
paper_title: Development of origin–destination matrices using mobile phone call data

paper_content:

Abstract In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.
---
paper_title: Circos: An information aesthetic for comparative genomics

paper_content:

We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.
---
paper_title: Scikit-learn: Machine Learning in Python

paper_content:

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
---
paper_title: Optimal Allocation of Truck Inspection Stations Based on k-Shortest Paths

paper_content:

Overweight trucks damage the road infrastructure, contribute to greenhouse gas emissions, and represent a potential treat to traffic safety. An efficient way of reducing the number of overweight trucks is to implement weigh-in-motion (WIM) systems that are designed to record axle and gross vehicle weights as they pass over a sensor. Although they are effective in detecting overweight trucks, WIM systems are costly and only their efficient allocation can justify the investment. So far several models were developed to optimize the location of WIM checkpoints, but they were all built on a simplifying assumption that trucks travel along the shortest paths between their origins and destinations. This paper proposes a model that allocates WIM checkpoints while considering that overweight trucks try to bypass checkpoints along the shortest unmonitored detours. The problem is formulated as a binary program and applied to minimize the damage due to overweight trucks. This proposed model is applied in a realistic case study on the road network of Nevada. The results imply that considerable savings can be achieved by optimally allocating WIM checkpoints and that the proposed model can provide a valuable decision support for government agencies involved in road infrastructure maintenance and control.
---
paper_title: The avoidance of weigh stations in Virginia by overweight trucks.

paper_content:

The primary objective of this research was to examine the avoidance of weigh stations in Virginia by overweight trucks. Secondary objectives were (1) to determine the magnitude of overweight truck activity on selected routes and (2) to compare traffic loading data collected using static scales with enforcement with data collected using weigh-in-motion without enforcement. Two weigh stations on 1-81 were studied for weigh station avoidance. It was found that 11 and 14 percent (respectively) of the trucks on routes used to bypass the Stephens City and Troutville stations were overweight. At the Stephens City station, 50 percent of the runbys (which are trucks that travel past the weigh station without being weighed because the entrance lane to the station is filled with a queue of trucks) were overweight on Sunday night. Based on the number and percentage of overweight runbys, there is a need to increase the truck weighing capacity of this weigh station. From 12 to 27 percent of the trucks on two primary routes and one interstate route were overweight. Traffic loadings collected with WIM without enforcement are 30 to 60 percent higher than loadings collected using static scales and enforcement.
---
paper_title: Evasive flow capture: Optimal location of weigh-in-motion systems, tollbooths, and security checkpoints

paper_content:

The flow-capturing problem FCP consists of locating facilities to maximize the number of flow-based customers that encounter at least one of these facilities along their predetermined travel paths. The FCP literature assumes that if a facility is located along or "close enough" to a predetermined path of a flow of customers, that flow is considered captured. However, existing models for the FCP do not consider targeted users who behave noncooperatively by changing their travel paths to avoid fixed facilities. Examples of facilities that targeted subjects may have an incentive to avoid include weigh-in-motion stations used to detect and fine overweight trucks, tollbooths, and security and safety checkpoints. This article introduces a new type of flow-capturing model, called the "evasive flow-capturing problem" EFCP, which generalizes the FCP and has relevant applications in transportation, revenue management, and security and safety management. We formulate deterministic and stochastic versions of the EFCP, analyze their structural properties, study exact and approximate solution techniques, and show an application to a real-world transportation network. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 651, 22-42. 2015
---
paper_title: OPTIMIZATION OF GRID TRANSIT SYSTEM IN HETEROGENEOUS URBAN ENVIRONMENT

paper_content:

Current analytic models for optimizing urban transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The model presented here shows how an optimization approach can be successful without oversimplifying the spatial characteristics and demand patterns of urban areas. This model is designed to optimize a grid transit system in a heterogeneous urban environment whose demand and supply characteristics may vary arbitrarily among adjacent zones. Network characteristics (route and station locations) and operating headways are found that minimize the total cost, including supplier and user costs. Irregular many-to-many demand patterns, zonal variations in route costs, and vehicle capacity constraints are considered in a sequential optimization process.
---
paper_title: DEVELOPMENT OF VT-MICRO MODEL FOR ESTIMATING HOT STABILIZED LIGHT DUTY VEHICLE AND TRUCK EMISSIONS

paper_content:

Abstract The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.
---

Title: Applications of Trajectory Data in Transportation: Literature Review and Maryland Case Study

Section 1: Introduction
Description 1: Introduce the importance of trajectory data in transportation and the objectives of the study.

Section 2: Literature Review
Description 2: Review the existing literature on the use of trajectory data in transportation across six major areas of transportation engineering.

Section 3: Demand Estimation
Description 3: Discuss how trajectory data is utilized for estimating transportation demand.

Section 4: Modeling Human Behavior
Description 4: Explore the use of trajectory data to quantify human behavior in transportation.

Section 5: Designing Public Transit
Description 5: Examine how trajectory data contributes to designing public transit systems.

Section 6: Traffic Performance Measurement and Prediction
Description 6: Analyze the role of trajectory data in measuring and predicting traffic performance.

Section 7: Environmental Impact
Description 7: Discuss the applications of trajectory data in assessing and mitigating transportation's environmental impact.

Section 8: Safety
Description 8: Review the use of trajectory data to enhance transportation safety and emergency response.

Section 9: Data
Description 9: Provide an overview of the dataset used in the study.

Section 10: Preprocessing
Description 10: Describe the preprocessing steps required for using the trajectory data.

Section 11: Database
Description 11: Explain the database setup used for storing and querying the trajectory data.

Section 12: Penetration Rate
Description 12: Detail the calculation and significance of the dataset's penetration rate.

Section 13: Methods
Description 13: Outline the machine learning and data visualization methods used in the study.

Section 14: Illustrative Case Study
Description 14: Present several applications of trajectory data in transportation using a case study from Maryland.

Section 15: Discussion
Description 15: Summarize the key findings and discuss potential challenges and recommendations for using trajectory data.

Section 16: Conclusions
Description 16: Conclude the paper by synthesizing the innovative applications of trajectory data and its future potential.