Short-term real-time traffic prediction methods: A survey
---
paper_title: Traffic Flow Forecast Survey

paper_content:

Short-term traffic flow forecasting is an important aspect of the ITS as traffic predication can alleviate congestion, which causes drivers to incur a longer traveling time and economical loses. In addition, traffic congestion increases the pollution and the fuel usage. Thus, it is one of the severe problems in Metropolitan areas. Further, in tunnels the forecasting may help scheduling the ventilation fans. This way, the ventilation cost might be decreased while the air quality increased. Additional aspect of traffic prediction is that it may enable the drivers to plan their departure time and traveling path, as they posses the predictive information. In this paper, we survey the different techniques used for traffic forecasting, the input data for these techniques, the output provided by them, as well as some general insights.
---
paper_title: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

paper_content:

Short term traffic flow forecasting has received sustained attention for its ability to provide the anticipatory traffic condition required for proactive traffic control and management. Recently, a stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) process has gained increasing notice for its ability to jointly generate traffic flow level prediction and associated prediction interval. Considering the need for real time processing, Kalman filters have been utilized to implement this SARIMA + GARCH structure. Since conventional Kalman filters assume constant process variances, adaptive Kalman filters that can update the process variances are investigated in this paper. Empirical comparisons using real world traffic flow data aggregated at 15-min interval showed that the adaptive Kalman filter approach can generate workable level forecasts and prediction intervals; in particular, the adaptive Kalman filter approach demonstrates improved adaptability when traffic is highly volatile. Sensitivity analyses show that the performance of the adaptive Kalman filter stabilizes with the increase of its memory size. Remarks are provided on improving the performance of short term traffic flow forecasting.
---
paper_title: Microscopic Traffic Simulation with Intelligent Agents

paper_content:

The subject of microscopic traffic simulation has gained increasing significance in recent years. It enables the testing of traffic scenarios in the laboratory and the evaluation of changes in the infrastructure prior to their physical realization, which saves time and cost. Additionally, agents play an important role in artificial intelligence and are emerging in other fields of science as well, including microscopic simulation of traffic networks. Using agents, it is possible to simulate different driver characteristics and hence it enables a realistic simulation of human driving behaviour. In this book a suitable architecture for a microscopic traffic simulation with intelligent agents is developed and the necessary simulation parameters and components are discussed. Simulation parameters are a very important part of the simulation, since they are the factors that influence vehicle drivers. Fuzzy logic is used to model these parameters to assure a human-like flow of information and to enable human reasoning. On this basis, a conceptual architecture that represents the interrelation between the single simulation components is developed.
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paper_title: Active Traffic Management as a Tool for Addressing Traffic Congestion

paper_content:

Recurrent and non-recurrent congestion in urban areas continues to be a major concern due to its adverse impacts on delays, fuel consumption and pollution, driver frustration, and traffic safety. In the U.S., limited public funding for roadway expansion and improvement projects, coupled with continued growth in travel along congested urban freeway corridors, creates a pressing need for innovative congestion management approaches.
---
paper_title: Traffic Flow Forecast Survey

paper_content:

Short-term traffic flow forecasting is an important aspect of the ITS as traffic predication can alleviate congestion, which causes drivers to incur a longer traveling time and economical loses. In addition, traffic congestion increases the pollution and the fuel usage. Thus, it is one of the severe problems in Metropolitan areas. Further, in tunnels the forecasting may help scheduling the ventilation fans. This way, the ventilation cost might be decreased while the air quality increased. Additional aspect of traffic prediction is that it may enable the drivers to plan their departure time and traveling path, as they posses the predictive information. In this paper, we survey the different techniques used for traffic forecasting, the input data for these techniques, the output provided by them, as well as some general insights.
---
paper_title: Real-time road traffic prediction with spatio-temporal correlations

paper_content:

Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.
---
paper_title: The Geography of Transport Systems

paper_content:

1. Transportation and Geography 2. Transportation Systems and Networks 3. Economic and Spatial Structure of Transport Systems 4. Transportation Modes 5. Transportation Terminals 6. International and Regional Transportation 7. Urban Transportation 8. Transport and Environment 9. Transport Planning and Policy Conclusion: Issues and Challenges in Transport Geography. Glossary and Index
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paper_title: Microscopic Traffic Simulation with Intelligent Agents

paper_content:

The subject of microscopic traffic simulation has gained increasing significance in recent years. It enables the testing of traffic scenarios in the laboratory and the evaluation of changes in the infrastructure prior to their physical realization, which saves time and cost. Additionally, agents play an important role in artificial intelligence and are emerging in other fields of science as well, including microscopic simulation of traffic networks. Using agents, it is possible to simulate different driver characteristics and hence it enables a realistic simulation of human driving behaviour. In this book a suitable architecture for a microscopic traffic simulation with intelligent agents is developed and the necessary simulation parameters and components are discussed. Simulation parameters are a very important part of the simulation, since they are the factors that influence vehicle drivers. Fuzzy logic is used to model these parameters to assure a human-like flow of information and to enable human reasoning. On this basis, a conceptual architecture that represents the interrelation between the single simulation components is developed.
---
paper_title: A Hidden Markov Model for short term prediction of traffic conditions on freeways

paper_content:

Accurate short-term prediction of traffic conditions on freeways and major arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. Given the dynamic and stochastic nature of freeway traffic, this study proposes a stochastic approach, Hidden Markov Model (HMM), for short-term freeway traffic prediction during peak periods. The data used in the study was gathered from real-time traffic monitoring devices over six years on a 60.8-km (38-mile) corridor of Interstate-4 in Orlando, Florida. The HMM defines traffic states in a two-dimensional space using first-order statistics (Mean) and second-order statistics (Contrast) of speed observations. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For a sequence of traffic speed observations, HMMs estimate the most likely sequence of traffic states. The model performance was evaluated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state in the two-dimensional space. Reasonable prediction errors lower than or around 10% were obtained from HMMs. Also, the model performance was not remarkably affected by location, travel direction, and peak period time. The HMMs were compared to two naive predication methods. The results showed that HMMs perform better and are more robust than the naive methods. Therefore, the study concludes that the HMM approach was successful in modeling short-term traffic condition prediction during peak periods and in accounting for the inherent stochastic nature of traffic conditions.
---
paper_title: Traffic Flow Forecast Survey

paper_content:

Short-term traffic flow forecasting is an important aspect of the ITS as traffic predication can alleviate congestion, which causes drivers to incur a longer traveling time and economical loses. In addition, traffic congestion increases the pollution and the fuel usage. Thus, it is one of the severe problems in Metropolitan areas. Further, in tunnels the forecasting may help scheduling the ventilation fans. This way, the ventilation cost might be decreased while the air quality increased. Additional aspect of traffic prediction is that it may enable the drivers to plan their departure time and traveling path, as they posses the predictive information. In this paper, we survey the different techniques used for traffic forecasting, the input data for these techniques, the output provided by them, as well as some general insights.
---
paper_title: Pattern-based short-term prediction of urban congestion propagation and automatic response

paper_content:

This paper presents a method for the online prediction of urban congestion patterns including their spatio-temporal propagation based on historic state data. Traffic state data for each link and time interval within the Berlin street network comes from a dynamic route choice and traffic assignment model. From extensive historic traffic state data congestion patterns are generated and classified in an appropriate manner. Based on this analysis, a method was developed to predict the propagation of congestion within the network based on pattern recognition. Significant parts of the network-wide prognosis are selected and sent as messages to the operator of the traffic management centre. A further step identifies actuators at in- and outflow areas of current and predicted congestion in order to increase the outflow from and decrease the inflow to the congested area. Messages for variable message signs are generated automatically and displayed to the operator with other appropriate measures. The work presented was carried out within the German research project IQ Mobility, which was funded by the initiative Verkehrsmanagement 2010 (Traffic Management 2010).
---
paper_title: Real-time travel time prediction using particle filtering with a non-explicit state-transition model

paper_content:

The research presented in this paper develops a particle filter approach for the real-time short to medium-term travel time prediction using real-time and historical data. Given the challenges in defining the particle filter time update process, the proposed algorithm selects particles from a historical database and propagates particles using historical data sequences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem by replacing invalid or low weighted particles with historical data that provide similar data sequences to real-time traffic measurements. As a result, each particle generates a predicted travel time with a corresponding weight that represents the level of confidence in the prediction. Consequently, the prediction can produce a distribution of travel times by aggregating all weighted particles. A 95-mile freeway stretch from Richmond to Virginia Beach along I-64 and I-264 is used to test the proposed algorithm. Both the absolute and relative prediction errors using the leave-one-out cross validation concept demonstrate that the proposed method produces the least deviation from ground truth travel times, compared to instantaneous travel times, two Kalman filter algorithms and a K nearest neighbor (k-NN) method. Moreover, the maximum prediction error for the proposed method is the least of all the algorithms and maintains a stable performance for all test days. The confidence boundaries of the predicted travel times demonstrate that the proposed approach provides good accuracy in predicting travel time reliability. Lastly, the fast computation time and online processing ensure the method can be used in real-time applications.
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paper_title: DynaMIT 2.0: The Next Generation Real-Time Dynamic Traffic Assignment System

paper_content:

Real-time transportation models are proven to be highly useful for traffic management and generation of traveler guidance information. The current state of the practice in real-time transportation modeling is represented by DynaMIT, which generates consistent anticipatory information about the future state of the transportation network based on current real-time data. DynaMIT has been effectively applied across a variety of locations and sensor configurations. The next generation of real-time models will be multi-modal and include representation of dynamic pricing and commercial vehicles. To support this, these models will be based on activity-based demand and will make use of the latest software design strategies, enhanced data availability and personal/vehicle connectivity.
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paper_title: Integration of Activity-Based Modeling and Dynamic Traffic Assignment

paper_content:

The traditional trip-based approach to transportation modeling has been used for the past 30 years. Because of limitations of traditional planning for short-term policy analysis, researchers have explored alternative paradigms for incorporating more behavioral realism in planning methodologies. On the demand side, activity-based approaches have evolved as an alternative to traditional trip-based transportation demand forecasting. On the supply side, dynamic traffic assignment models have been developed as an alternative to static assignment procedures. Much of the research effort in activity-based approaches (the demand side) and dynamic traffic assignment techniques (the supply side) has been undertaken relatively independently. To maximize benefits from these advanced methodologies, it is essential to combine them through a unified framework. The objective of this paper is to develop a conceptual framework and explore practical integration issues for combining the two streams of research. Technical, com...
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paper_title: AN EVALUATION OF TRAFFIC SIMULATION MODELS FOR SUPPORTING ITS DEVELOPMENT

paper_content:

In this report, the authors evaluate existing traffic simulation models in order to identify those models that can be potentially applied in Intelligent Transportation Systems (ITS) equipped networks. The models are categorized according to type (macroscopic microscopic, or mesoscopic), as well as functionality (highway signal, integrated). The evaluation is conducted through two steps: initial screening and in-depth evaluation. From the research, it is concluded that the CORSIM and INTEGRATION models appear to have the highest probability of success in real-world applications. The authors also find that by adding more calibration and validation in the U.S., the AIMSUN 2 and the PARAMICS models will be brought to the forefront in the near term for use with ITS applications.
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paper_title: Calibration and validation of TRANSIMS microsimulator for an urban arterial network

paper_content:

TRANSIMS, a travel demand modeling software package initially developed by the Los Alamos National Laboratory (LANL), enables modeling of individual activities and provides second-by-second simulation results on vehicular movements. TRANSIMS has been applied more than a decade, but calibration and validation of TRANSIMS Microsimulator have not received proper attention from transportation engineering community. This paper presents a case study of a TRANSIMS Microsimulator calibration and validation using an experimental design approach. An urban arterial network consisted of four signalized intersections was calibrated against field measured travel times and traffic count data. The distributions of travel times and traffic count obtained from the multiple replications of the default parameter values were not able to replicate field conditions, while the proposed approach did well. Even though this case study used a small corridor network in urban area, the calibration and validation procedure could be extended to a larger scale network in TRANSIMS modeling as well as transportation planning applications.
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paper_title: A cellular automaton model for freeway traffic

paper_content:

We introduce a stochastic discrete automaton model to simulate freeway traffic. Monte-Carlo simulations of the model show a transition from laminar traffic flow to start-stop- waves with increasing vehicle density, as is observed in real freeway traffic. For special cases analytical results can be obtained.
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paper_title: Parallel DYNEMO : meso-scopic traffic flow simulation on large networks

paper_content:

Traffic flow simulation is recently being applied to types of studies which require at the same time a large study area and models deeper than macroscopic assignments. Examples are investigations of the effects dynamic route guidance over large networks and the coupling with environmental models on large domains. The computational complexity can be reduced first by moving from microscopic to mesoscopic traffic flow models of the Payne-Cremer type. Where still more computing power is needed, parallelisation of the model may offer a solution. This paper describes a particular mesoscopic traffic flow model and its parallelisation undertaken in the ESPRIT project SIMTRAP. A new two step decomposition strategy is detailed along with its implementation using the message passing model PVM. The resulting speed-up is reported, drawing on both theoretical considerations and on measurements taken in the SIMTRAP demonstrator applications. The paper concludes with lessons learned from the experiments and directions for future work.
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paper_title: A Review of Models of Urban Traffic Networks (With Particular reference to the Requirements for Modelling Dynamic Route Guidance Systems)

paper_content:

This paper reviews a number of existing models of urban traffic networks developed in Europe and North America. The primary intention is to evaluate the various models with regard to their suitability to simulate traffic conditions and driver behavior when a dynamic route guidance system is in operation.
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paper_title: DYNEMO: A MODEL FOR THE SIMULATION OF TRAFFIC FLOW IN MOTORWAY NETWORKS

paper_content:

The paper presents the simulation model dynemo which has been designed for the development, evaluation and optimization of traffic control systems for motorway networks. A new traffic flow model included with the simulation package combines the advantages of a macroscopic model (computational simplicity) with the advantages of a microscopic model (output statistics relating to individual vehicles). For each stretch in the network, the model needs as input a relationship between traffic density and mean speed and the distribution of free flow speeds. The new traffic flow model is validated by use of an example. The simulation package is implemented on a 16-bit microcomputer. A real network with a traffic control system has been simulated with the model. (Author/TRRL)
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paper_title: VISUM-ONLINE - TRAFFIC MANAGEMENT FOR THE EXPO 2000 BASED ON A TRAFFIC MODEL

paper_content:

This year the World Exposition is being held in Hanover, Germany. A number of measures were proposed to handle the expected surplus traffic. This article describes the core system for the traffic control and traffic information centre as it is being implemented in Hanover. The objectives of the traffic control centre include providing reliable prediction of travel times, alternative routing advice, mode choice information, parking guidance as well as reducing disruption caused by incidents. These objectives are tackled by linking various traffic data measurements techniques, a number of ITS traffic control measures and a realtime traffic simulation model. A simulation model called VISUM-online considers all available historical and present traffic data of the Hanover region and uses traffic flow models to predict the current and future situation. The Path Flow Estimator is used to update historical origin-destination-matrices using current traffic counts and assignment modelling. For the covering abstract see ITRD E114174.
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paper_title: An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction

paper_content:

Abstract In order to accurately predict the short-term traffic flow, this paper presents a k-nearest neighbor (KNN) model. Short-term urban expressway flow prediction system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the predication plan. At first, preprocess the original data and then standardized the effective data in order to avoid the magnitude difference of the sample data and improve the prediction accuracy. At last, a short-term traffic prediction based on k-NN nonparametric regression model is developed in the Matlab platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the predicting result is analyzed. Results show that the accuracy of the proposed method is over 90 percent and it also rereads that the feasibility of the methods is used in short-term traffic flow prediction.
---
paper_title: A Hidden Markov Model for short term prediction of traffic conditions on freeways

paper_content:

Accurate short-term prediction of traffic conditions on freeways and major arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. Given the dynamic and stochastic nature of freeway traffic, this study proposes a stochastic approach, Hidden Markov Model (HMM), for short-term freeway traffic prediction during peak periods. The data used in the study was gathered from real-time traffic monitoring devices over six years on a 60.8-km (38-mile) corridor of Interstate-4 in Orlando, Florida. The HMM defines traffic states in a two-dimensional space using first-order statistics (Mean) and second-order statistics (Contrast) of speed observations. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For a sequence of traffic speed observations, HMMs estimate the most likely sequence of traffic states. The model performance was evaluated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state in the two-dimensional space. Reasonable prediction errors lower than or around 10% were obtained from HMMs. Also, the model performance was not remarkably affected by location, travel direction, and peak period time. The HMMs were compared to two naive predication methods. The results showed that HMMs perform better and are more robust than the naive methods. Therefore, the study concludes that the HMM approach was successful in modeling short-term traffic condition prediction during peak periods and in accounting for the inherent stochastic nature of traffic conditions.
---
paper_title: Freeway traffic estimation in Beijing based on particle filter

paper_content:

Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed. Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.
---
paper_title: Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach

paper_content:

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction perfor- mance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.
---
paper_title: Real-time travel time prediction using particle filtering with a non-explicit state-transition model

paper_content:

The research presented in this paper develops a particle filter approach for the real-time short to medium-term travel time prediction using real-time and historical data. Given the challenges in defining the particle filter time update process, the proposed algorithm selects particles from a historical database and propagates particles using historical data sequences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem by replacing invalid or low weighted particles with historical data that provide similar data sequences to real-time traffic measurements. As a result, each particle generates a predicted travel time with a corresponding weight that represents the level of confidence in the prediction. Consequently, the prediction can produce a distribution of travel times by aggregating all weighted particles. A 95-mile freeway stretch from Richmond to Virginia Beach along I-64 and I-264 is used to test the proposed algorithm. Both the absolute and relative prediction errors using the leave-one-out cross validation concept demonstrate that the proposed method produces the least deviation from ground truth travel times, compared to instantaneous travel times, two Kalman filter algorithms and a K nearest neighbor (k-NN) method. Moreover, the maximum prediction error for the proposed method is the least of all the algorithms and maintains a stable performance for all test days. The confidence boundaries of the predicted travel times demonstrate that the proposed approach provides good accuracy in predicting travel time reliability. Lastly, the fast computation time and online processing ensure the method can be used in real-time applications.
---
paper_title: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

paper_content:

Short term traffic flow forecasting has received sustained attention for its ability to provide the anticipatory traffic condition required for proactive traffic control and management. Recently, a stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) process has gained increasing notice for its ability to jointly generate traffic flow level prediction and associated prediction interval. Considering the need for real time processing, Kalman filters have been utilized to implement this SARIMA + GARCH structure. Since conventional Kalman filters assume constant process variances, adaptive Kalman filters that can update the process variances are investigated in this paper. Empirical comparisons using real world traffic flow data aggregated at 15-min interval showed that the adaptive Kalman filter approach can generate workable level forecasts and prediction intervals; in particular, the adaptive Kalman filter approach demonstrates improved adaptability when traffic is highly volatile. Sensitivity analyses show that the performance of the adaptive Kalman filter stabilizes with the increase of its memory size. Remarks are provided on improving the performance of short term traffic flow forecasting.
---
paper_title: An Aggregation Approach to Short-Term Traffic Flow Prediction

paper_content:

In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naive, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.
---
paper_title: Real-time road traffic prediction with spatio-temporal correlations

paper_content:

Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.
---
paper_title: THE IMPACT OF TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE-USING COMPETENCE SETS ANALYSIS

paper_content:

Generally speaking, drivers' route choice is a fuzzy problem. However, if drivers' habitual domain becomes stable and without significant stimuli, route choice becomes a routine problem. Route choice can become fuzzy again if drivers perceive information stimuli. Intuitively, traffic information should help drivers to reach destination in an efficient way, but the abundant and complex information could overwhelm the drivers. In this work, a different and novel approach to complement the driver route choice decision models is exploited. The concept of habitual domains and competence sets proposed by Yu in 1980 is applied to the route choice problem. The effect of traffic information on route choice is isolated to analysis the behavior of route choice decision. Performance indexes of route choice decision are developed to help the drivers or the traffic information providers in expanding their competence sets to fully address the needs of route choice decision.
---
paper_title: Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach

paper_content:

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction perfor- mance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.
---
paper_title: Real-time road traffic prediction with spatio-temporal correlations

paper_content:

Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.
---
paper_title: An Aggregation Approach to Short-Term Traffic Flow Prediction

paper_content:

In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naive, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.
---
paper_title: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

paper_content:

Short term traffic flow forecasting has received sustained attention for its ability to provide the anticipatory traffic condition required for proactive traffic control and management. Recently, a stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) process has gained increasing notice for its ability to jointly generate traffic flow level prediction and associated prediction interval. Considering the need for real time processing, Kalman filters have been utilized to implement this SARIMA + GARCH structure. Since conventional Kalman filters assume constant process variances, adaptive Kalman filters that can update the process variances are investigated in this paper. Empirical comparisons using real world traffic flow data aggregated at 15-min interval showed that the adaptive Kalman filter approach can generate workable level forecasts and prediction intervals; in particular, the adaptive Kalman filter approach demonstrates improved adaptability when traffic is highly volatile. Sensitivity analyses show that the performance of the adaptive Kalman filter stabilizes with the increase of its memory size. Remarks are provided on improving the performance of short term traffic flow forecasting.
---
paper_title: Real-time travel time prediction using particle filtering with a non-explicit state-transition model

paper_content:

The research presented in this paper develops a particle filter approach for the real-time short to medium-term travel time prediction using real-time and historical data. Given the challenges in defining the particle filter time update process, the proposed algorithm selects particles from a historical database and propagates particles using historical data sequences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem by replacing invalid or low weighted particles with historical data that provide similar data sequences to real-time traffic measurements. As a result, each particle generates a predicted travel time with a corresponding weight that represents the level of confidence in the prediction. Consequently, the prediction can produce a distribution of travel times by aggregating all weighted particles. A 95-mile freeway stretch from Richmond to Virginia Beach along I-64 and I-264 is used to test the proposed algorithm. Both the absolute and relative prediction errors using the leave-one-out cross validation concept demonstrate that the proposed method produces the least deviation from ground truth travel times, compared to instantaneous travel times, two Kalman filter algorithms and a K nearest neighbor (k-NN) method. Moreover, the maximum prediction error for the proposed method is the least of all the algorithms and maintains a stable performance for all test days. The confidence boundaries of the predicted travel times demonstrate that the proposed approach provides good accuracy in predicting travel time reliability. Lastly, the fast computation time and online processing ensure the method can be used in real-time applications.
---
paper_title: Freeway traffic estimation in Beijing based on particle filter

paper_content:

Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed. Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.
---
paper_title: Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach

paper_content:

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction perfor- mance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.
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paper_title: Freeway traffic estimation in Beijing based on particle filter

paper_content:

Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed. Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.
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Title: Short-term real-time traffic prediction methods: A survey

Section 1: INTRODUCTION
Description 1: Introduce the background and motivation for short-term real-time traffic prediction methods and outline the contributions of the paper.

Section 2: PRELIMINARIES
Description 2: Discuss important concepts related to traffic prediction and contextualize forecasting systems.

Section 3: CHARACTERIZATION OF PREDICTIVE METHODS
Description 3: Detail the characteristics relevant for estimation in real-time systems and describe the application context and motivation, including the collection of real-time data, prediction targets, and measuring prediction accuracy.

Section 4: MODEL DRIVEN
Description 4: Present and analyze model-driven approaches and simulators used for traffic prediction.

Section 5: DATA DRIVEN
Description 5: Describe recent data-driven approaches to short-term traffic prediction.

Section 6: DISCUSSION
Description 6: Summarize the main findings, observations, and provide insights and future directions for developing short-term traffic prediction systems.

Section 7: CONCLUSION
Description 7: Summarize the literature review and related work about traffic prediction models, comparing model-driven and data-driven approaches, and suggest areas for future work.