Survey of ETA prediction methods in public transport networks.
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paper_title: The desired quality of integrated multimodal travel information in public transport: Customer needs for time and effort savings

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Travel information is one of the factors that contribute to the quality of public transport. In particular, integrated multimodal travel information (IMTI) is expected to affect customers' modal choice. The objective of this research is to identify customers' desired quality of IMTI provision in public transport. Customers' desired IMTI quality can vary throughout the pre-trip, wayside and on-board stages of a journey. The main determinants are time savings (travel and search time) and effort savings (physical, cognitive, and affective effort). In a sample of Dutch travellers with a substantial share of young persons, the pre-trip stage turns out to be the favourite stage to collect IMTI when planning multimodal travel; desired IMTI types in this stage are used to plan the part of the journey that is made by public transport. Wayside IMTI is most desired when it helps the traveller to catch the right vehicle en route. On-board travellers are most concerned about timely arrival at interchanges in order to catch connecting modes. In the whole travel process, travel time is the most important saving. Apart from that, pre-trip search time savings are also desired, while en route affective effort is more important than cognitive effort.
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paper_title: Automatic Transport Network Matching Using Deep Learning

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Public transport users are increasingly expecting better service and up to date information, in pursuit of a seamless journey experience. In order to meet these expectations, many transport operators are already offering free mobile apps to help customers better plan their journeys and access real-time travel information. Leveraging the spatio-temporal data that such apps can produce at scale (i.e. timestamped GPS traces), opens an opportunity to bridge the gap between passenger expectations and capabilities of the operators by providing a real-time 360-degree view of the transport network based on the ‘Apps as infrastructure’ paradigm. The first step towards fulfilling this vision is to understand which routes and services the passengers are travelling on at any given time. Mapping a GPS trace onto a particular transport network is known as ‘network matching’. In this paper, the problem is formulated as a supervised sequence classification task, where sequences are made of geographic coordinates, time, and line and direction of travel as a label. We present and compare two data-driven approaches to this problem: (i) a heuristic algorithm, which looks for nearby stops and makes an estimation based on their timetables -- used as a baseline -- and (ii) a deep learning approach using a recurrent neural network (RNN). Since RNNs require considerable amounts of data to train a good model, and collecting and labelling this data from real users is a challenging task (e.g. asking too often can be overwhelming; privacy concerns on providing GPS location; not reliable labels due to mistakes or misuse), one of our contributions is a synthetic journey data generator. The datasets that we generated have been made as realistic as possible by querying real timetables and adding position and temporal noise to simulate variable GPS accuracy and vehicle delays, sampled from empirical distributions estimated using thousands of real location reports. To validate our approach we have used a separate dataset made of hundreds of real user journeys provided by a UK-based bus operator. Our experimental results are promising and our next step is to deploy a solution in a production environment. From the operator’s point of view, this will enable multiple smart applications like account based ticketing, identification of disruptions, real-time passenger counting, and network analysis. Passengers will also, therefore, benefit from a better service and an increase in the quality of information due to leveraging such big data processing.
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paper_title: Bus arrival time prediction at any distance of bus route using deep neural network model

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A precise prediction of transportation time is important to help both passengers to plan their trips and bus operations control to make an effective fleet management. In this study, we make use of GPS data from a public transportation bus line to develop a public bus arrival time prediction at any distance along the route. With large and complex information, deep neural network model (DNN) is used to get high prediction accuracy. In this paper, variables and structures of the proposed DNN model are presented. The performance of the proposed model is evaluated by conducting real BMTA-8 bus data in Bangkok, Thailand, and comparing the result with the currently used ordinary least square (OLS) regression model. The result shows that the proposed DNN model is more accurate than the OLS regression model around 55% for mean absolute percentage error (MAPE). It outperforms the current prediction method of the studied bus line, and it is feasible and applicable for bus travel time prediction of any route.
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paper_title: Real time prediction of bus arrival time: A review

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Bus Transportation plays an important role in modern society and has been developed in many parts of the world. It reduces the private vehicle usage; fuel consumption and more over reduce traffic congestion, if the arrival time of the buses is accurate. In this paper, various literatures have been surveyed which is used for prediction of bus arrival time. Real time prediction of arrival time is so much valuable for passengers and transport departments. It reduces the waiting time which they have faced during trips and having a satisfaction to know about the schedule of the bus.
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paper_title: Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders

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In order to attract more choice riders, transit service must not only have a high level of service in terms of frequency and travel time but also must be reliable. Although transit agencies continuously work to improve on-time performance, such efforts often come at a substantial cost. One inexpensive way to combat the perception of unreliability from the user perspective is real-time transit information. The OneBusAway transit traveler information system provides real-time next bus countdown information for riders of King County Metro via website, telephone, text-messaging, and smart phone applications. Although previous studies have looked at traveler response to real-time information, few have addressed real-time information via devices other than public display signs. For this study, researchers observed riders arriving at Seattle-area bus stops to measure their wait time while asking a series of questions, including how long they perceived that they had waited. The study found that for riders without real-time information, perceived wait time is greater than measured wait time. However, riders using real-time information do not perceive their wait time to be longer than their measured wait time. This is substantiated by the typical wait times that riders report. Real-time information users say that their average wait time is 7.5Â min versus 9.9Â min for those using traditional arrival information, a difference of about 30%. A model to predict the perceived wait time of bus riders was developed, with significant variables that include the measured wait time, an indicator variable for real-time information, an indicator variable for PM peak period, the bus frequency in buses per hour, and a self-reported typical aggravation level. The addition of real-time information decreases the perceived wait time by 0.7Â min (about 13%). A critical finding of the study is that mobile real-time information reduces not only the perceived wait time, but also the actual wait time experienced by customers. Real-time information users in the study wait almost 2Â min less than those arriving using traditional schedule information. Mobile real-time information has the ability to improve the experience of transit riders by making the information available to them before they reach the stop.
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paper_title: Passenger Wait Time Perceptions at Bus Stops Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real- Time Bus Arrival Information

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This study quantifies the relationship between the perceived and actual waiting times experienced by passengers at a bus stop. Understanding such a relationship would be useful in quantifying the value of providing real-time information to passengers on the time until the next bus is expected to arrive at a bus stop. Data on perceived and actual passenger waiting times, along with socioeconomic characteristics, were collected at bus stops where no real-time bus arrival information is provided, and relationships between perceived and actual waiting times are estimated. The results indicate that passengers do perceive time to be greater than the actual amount of time waited. However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes). Assuming that a passenger’s perceived waiting time is equal to the actual time when presented with accurate real-time bus arrival information, the value of the eliminated additional time is assessed in the form of reduced vehicle hours per day resulting from a longer headway that produces the same mean passenger waiting time. The eliminated additional time is also assessed in the form of uncertainty in the headway resulting in the same extra waiting time. Naturally, such benefits of passenger information can only be confirmed when the actual effect of information on the perception of waiting time is quantified.
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paper_title: Predicting Bus Real-time Travel Time Basing on both GPS and RFID Data

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Instantaneous and accurate prediction of bus arrival time can help improve the quality of bus-arrival-time information service, and attract additional ridership. On the basis of bus running processes, a self-adaptive exponential smoothing algorithm is proposed to predict the bus running speed based on the short-term running speeds of taxis and buses available. And a bus travel time prediction model is proposed, in which the delay caused by the signal control and the acceleration and deceleration are considered. The research results show that there is a significant linear correlation between speeds of buses and taxis on the same link during the same time period, and the overall performance of the radio frequency identification (RFID)-data-based model is superior to that of automatic vehicle location (AVL)- data-only-based model, regardless of whether the traffic congested or not.
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paper_title: Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms

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Most transit agencies are trying to increase their ridership. To achieve this goal, they are looking to maintain or even improve their level of service. This is very hard, since traffic congestion is normally increasing. As a result, bus travel times are higher and less reliable, which makes harder to predict travel times and avoid bunching. Being able to accurately predict bus travel speeds and update this prediction with real-time information could improve the quality and reliability of the information given to users, and increase the effectiveness of control schemes.
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paper_title: Multi-label classification of estimated time of arrival with ensemble neural networks in bus transportation network

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The arrival times of buses are often hard to predict due to variation of real time traffic conditions, deployment schedules and traffic incidents. The provision of timely arrival time information is thus vital for passengers to minimize their waiting time and improve riders' confidence in the public transportation system, directly promoting more ridership. Multiple buses are commonly observed to arrive at a bus stop every hour. In this research, the prediction of estimated time of arrival (ETA) of buses is translated into a multi-label classification problem. Using buses historical global positioning system (GPS) arrival time, neural network models (ANN) are shown to be reliable solutions for the problem, and ensemble of neural networks are explored for more relevant output. The experimental results demonstrate that 77–78% of the time, ANN models are able to accurately predict arrival time of buses. The neural network models are able to outperform the other algorithms (i.e. Decision Tree, Random Forest, Naive Bayes) in the classification of multi-label arrival times by up to 8% based on performance metrics such as hamming loss, accuracy, and F 1 scores. Among the ensemble models, Random A-labelsets (RAkEL) with 2-labelsets and 3-labelsets outperform the single neural network by 2–3% in multi-label accuracy and F1 scores. The findings are convincing for us to further explore other problem transformation and ensemble approaches in the future.
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paper_title: Estimation of bus travel time incorporating dwell time for APTS applications

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Congestion has become a serious problem in the context of urban transport around the world. As more and more vehicles are being introduced into the urban streets every year, the mode share of the public transportation sector is declining at an alarming rate. Particularly in developing countries, more people have moved to personalized mode since it is becoming easily affordable and the quality of service offered by the public transit is not improving. To attract more people, the public transit should provide a high level of quality service to the passengers. One way of achieving this is by using Advanced Public Transport Systems (APTS) applications such as providing accurate real-time bus arrival information to the passengers which will improve the service reliability of the public transit. Travel time prediction has been a well-renowned topic of research for years. However, studies which were model based and incorporating dwell times at bus stops explicitly for heterogeneous traffic conditions are limited. The present study tries to explicitly incorporate the bus stop delays associated with the total travel times of the buses under heterogeneous traffic conditions. This will help in obtaining a reliable algorithm which can be adopted for bus arrival time prediction under Indian conditions.
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paper_title: Prediction of Bus Travel Time Using Artificial Neural Network

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The objective of this study is to apply artificial neural network (ANN) for development of bus travel time prediction model. The bus travel time prediction model was developed to give real time bus arrival information to the passenger and transit agencies for applying proactive strategies. For development of ANN model, dwell time, delays and distance between the bus stops was taken as input data. Arrivals/departure times, delays, average speed between the bus stop and distance between the bus stops were collected for two urban routes in Delhi. Model was developed, validated and tested using GPS (Global Positioning System) data collected from field study. Comparative study reveals that ANN model outperformed the regression model in terms of accuracy and robustness.
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paper_title: Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems

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This research effort uses AVL and APC dynamic data to develop a bus travel time model capable of providing real time information on bus arrival times to passengers, via traveler information services and to transit controllers for the application of proactive control strategies. The developed model is based on two Kalman filter algorithms for the prediction of running times and dwell times alternately in an integrated framework. The AVL and APC data used were obtained for a specific bus route in downtown Toronto. The performance of the developed prediction model was tested using "hold out" data and other data from microsimulation representing different scenarios of bus operation along the investigated route using the VISSIM microsimulation software package. The Kalman Filter algorithm outperformed all other developed models in terms of accuracy demonstrating the dynamic ability to update itself based on new data that reflected the changing characteristics of the transit-operating environment. A user-interactive system was developed to provide continuous information on the expected arrival time of buses at downstream stops, hence the expected deviations from schedule. The system enables the user to assess in real time transit stop-based control actions to avoid such deviations before their occurrence hence allowing for proactive control, as opposed to the traditional reactive control, which attempts to recover the schedule after deviations occur
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paper_title: Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

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Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.
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paper_title: Using automatic passenger counter data in bus arrival time prediction

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Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.
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paper_title: Bus arrival time prediction using artificial neural network model

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A major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The objectives of this research are to develop and apply a model to predict bus arrival time using automatic vehicle location (AVL) data. In this research, the travel time prediction model considered schedule adherence and dwell times. Actual AVL data from a bus route located in Houston, Texas was used as a test bed. A historical data based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed the historical data based model and the regression models in terms of prediction accuracy.
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paper_title: A Self-learning algorithm for predicting bus arrival time based on historical data model

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The provision of timely and accurate bus arrive time information is very important. It helps to attract additional ridership and increase the satisfaction of transit users. In this paper, a self-learning prediction algorithm is proposed based on historical data model. Locations and speeds of the bus are periodically obtained from GPS senor installed on the bus and stored in database. Historical travel time in all road sections is collected. These historical data are trained using BP neural network to predict the average speed and arrival time of the road sections. Experimental results indicate that the proposed algorithm achieves outstanding prediction accuracy compared with general solutions based on historical travel time.
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paper_title: Bus arrival time prediction at any distance of bus route using deep neural network model

paper_content:

A precise prediction of transportation time is important to help both passengers to plan their trips and bus operations control to make an effective fleet management. In this study, we make use of GPS data from a public transportation bus line to develop a public bus arrival time prediction at any distance along the route. With large and complex information, deep neural network model (DNN) is used to get high prediction accuracy. In this paper, variables and structures of the proposed DNN model are presented. The performance of the proposed model is evaluated by conducting real BMTA-8 bus data in Bangkok, Thailand, and comparing the result with the currently used ordinary least square (OLS) regression model. The result shows that the proposed DNN model is more accurate than the OLS regression model around 55% for mean absolute percentage error (MAPE). It outperforms the current prediction method of the studied bus line, and it is feasible and applicable for bus travel time prediction of any route.
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paper_title: Prediction model of bus arrival and departure times using avl and apc data

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The emphasis of this research effort was on using automatic vehicle location (AVL) and Automatic Passenger Counter (APC) dynamic data to develop a bus travel time model capable of providing real time information on bus arrival and departure times to passengers and to transit controllers for the application of proactive control strategies. The developed model is comprised of two Kalman filter algorithms for the prediction of running times and dwell times alternately in an integrated framework. The AVL and APC data used were obtained for a specific bus route in downtown Toronto. The performance of the prediction model was tested using "hold out" data and other data from a microsimulation model representing different scenarios of bus operation along the investigated route using the VISSIM microsimulation software package. The Kalman filter-based model outperformed other conventional models in terms of accuracy, demonstrating the dynamic ability to update itself based on new data that reflected the changing characteristics of the transit operating environment.
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paper_title: Hybrid model for prediction of bus arrival times at next station

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Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time-of-day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering-based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.
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paper_title: A Genetic Algorithm-based Method for Improving Quality of Travel Time Prediction Intervals

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Abstract The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.
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paper_title: Historical data based real time prediction of vehicle arrival time

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In recent times, most of the industries provide transportation facility for their employees from scheduled pick-up and drop points. In order to reduce longer waiting time, it is important to accurately predict the vehicle arrival in real time. This paper proposes a simple, lightweight yet powerful historical data based vehicle arrival time prediction model. Unlike previous work, the proposed model uses very limited input features namely vehicle trajectory and timestamp considering the scarcity and unavailability of data in the developing countries regarding traffic congestion, weather, scheduled arrival time, leg time, dwell time etc. The authors proposed model is evaluated against standard Artificial Neural Network (ANN) and Support Vector Machine (SVM) regression models using real bus data of an industry campus at Siruseri, Chennai collected over four months of time period. The result shows that proposed historical data based model can predict two and half (approx.) times faster than ANN model and two (approx.) times faster than SVM model while it also achieves a comparable accuracy (75.56%) with respect to ANN model (76%) and SVM model (71.3%). Hence, the proposed historical data based model is capable of providing a real time system by balancing the trade-off between prediction time and prediction accuracy.
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paper_title: Transit Vehicle Arrival Prediction: Algorithm and Large-Scale Implementation

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An algorithm is presented to predict transit vehicle arrival times up to 1 h in advance. It uses the time series of data from an automated vehicle location system, consisting of time and location pairs. These data are used with historical statistics in an optimal filtering framework to predict future arrivals. The algorithm is implemented for a large transit fleet in Seattle, Washington, and the prediction results for hundreds of locations are made widely available on the Web. An evaluation of the second busiest but most complex prediction site is presented to demonstrate the value of prediction over the use of schedules alone.
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paper_title: EasyComeEasyGo: Predicting Bus Arrival Time with Smart Phone

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Public transport, especially the bus transport, has been well developed in most cities. Thus the technique of bus arrival time prediction has become a research hotspot nowadays. It is a very important subject to improve the precision and reliability of the bus arrival time prediction, reduce travelers' anxieties and waiting times at bus stop. In this paper, we propose EasyComeEasyGo(ECEG), a novel bus arrival time prediction system based on smart phone for passengers who are traveling with bus in real-life scenarios. This study presents an algorithm that uses real-time GPS data from field and takes delays automatically into account for an accurate prediction of bus arrival time. We develop a prototype system with different types of android-based smart phone and actual GPS data from bus route 26 located in Dalian, China are used as a test bed. We have extensively evaluated the ECEG system with the bus route 26 over a month period. Our results suggest that the proposed system achieves outstanding prediction accuracy and gains most travelers' satisfy.
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paper_title: A prescription for transit arrival / departure prediction using automatic vehicle location data

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In this paper we present a general prescription for the prediction of transit vehicle arrival/departure. The prescription identifies the set of activities that are necessary to preform the prediction task, and describes each activity in a component based framework. We identify the three components, a Tracker, a Filter, and a Predictor, necessary to use automatic vehicle location (AVL) data to position a vehicle in space and time and then predict the arrival/departure at a selected location. Data, starting as an AVL stream, flows through the three components, each component transforms the data, and the end result is a prediction of arrival/departure. The utility of this prescription is that it provides a framework that can be used to describe the steps in any prediction scheme. We describe a Kalman filter for the Filter component, and we present two examples of algorithms that are implemented in the Predictor component. We use these implementations with AVL data to create two examples of transit vehicle prediction systems for the cities of Seattle and Portland.
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paper_title: An Arrival Time Prediction Method for Bus System

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This letter proposes random neural networks (RNNs) to randomly train several neural network (NN) models for the promotion of traditional NN. Moreover, an arrival time prediction method (ATPM) based on RNNs is proposed to predict the stop-to-stop travel time for motor carriers. In experiments, the results showed that the average accuracies of RNNs are 94.75% for highway and 78.22% for urban road, respectively. Furthermore, the accuracies of the proposed ATPM are higher than previous data mining methods. Therefore, the proposed ATPM is suitable to predict the stop-to-stop travel time for motor carriers.
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paper_title: ARIMA MODELS FOR BUS TRAVEL TIME PREDICTION

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In this paper, the time series model, Autoregressive Integrated Moving Average (ARIMA) is used to predict bus travel time. ARIMA model is simpler used for predicting bus travel time based on travel time series data (historic data) compared to regression method as the factors affecting bus travel time are not available in detail such as delay at link, bus stop, intersection, etc. Bus travel time prediction is an important aspect to bus operator in providing timetable for bus operation management and user information. The study aims at finding appropriate time series model for predicting bus travel time by evaluating the minimum of mean absolute relative error (MARE) and mean absolute percentage prediction error (MAPPE). In this case, data set was collected from the bus service operated on a divided 4-lane 2-way highway in Ipoh-Lumut corridor, Perak, Malaysia. The estimated parameters, appropriate model, and measures of model performance evaluation are presented. The analysis of both Ipoh to Lumut and Lumut to Ipoh directions is separately performed. The results show that the predicted travel times by using the moving average, MA(2) and MA(1) model, clearly fit with the observed values for both directions, respectively. These appropriate models are indicated by the minimum MARE and MAPPE values among the tentative models. It is concluded that MA(2) and MA(1) models are able to be appropriately applied in this case, and those models can be used for bus travel time prediction which helping in the timetable design or setup.
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paper_title: Real time bus travel time prediction using k-NN classifier

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AbstractPredicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy.
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paper_title: Bus arrival time prediction with real-time and historic data

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Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. GPS-equipped buses can be regarded as mobile sensors probing traffic flows on road surfaces. In this paper, we present an approach that predicts bus arrival time in terms of the knowledge learned from a large number of historical bus GPS trajectories. In our approach, we build time-dependent path-section graph, where a path-section is a road segment between two adjacent bus stops, to model the properties of dynamic road networks. Then, a clustering approach is designed to estimate the distribution of travel time on each path-section in different time slots. Finally, bus arrival time is predicted based on the path-section graph and real-time GPS information. Using a real-world trajectory dataset generated by 1000 buses in a period of 2 months, a bus arrival time prediction system is built. Then we evaluate the system with extensive experiments and realistic evaluations. Experiments show that our method is close to the actual value and better than some typical algorithms.
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paper_title: Predicting arrival times of buses using real-time GPS measurements

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Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. In this paper, we describe an efficient non-parametric algorithm which provides highly accurate predictions based on real-time GPS measurements. The key idea is to use a Kernel Regression model to represent the dependencies between position updates and the arrival times at bus stops. The performance of the proposed algorithm is evaluated on real data from the public bus transportation system in Dublin, Ireland. For a time horizon of 50 minutes, the prediction error of the algorithm is less than 10 percent on average. It clearly outperforms parametric methods which use a Linear Regression model, predictions based on the K-Nearest Neighbor algorithm, and a system which computes predictions of arrival times based on the current delay of buses. A study investigating the selection of interpolation points to reduce the size of the training set concludes the paper.
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paper_title: Prediction of Bus Travel Time Using Random Forests Based on Near Neighbors

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The prediction of bus arrival time is important for passengers who want to determine their departure time and reduce anxiety at bus stops that lack timetables. The random forests based on the near neighbor (RFNN) method is proposed in this article to predict bus travel time, which has been calibrated and validated with real-world data. A case study with two bus routes is conducted, and the proposed RFNN is compared with four methods: linear regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and classic random forest (RF). The results indicate that the proposed model achieves high accuracy. That is, one bus route has the results of 13.65 mean absolute error (MAE), 6.90% mean absolute percentage error (MAPE), 26.37 root mean squared error (RMSE) and 13.77 (MAE), 7.58% (MAPE), 29.01 (RMSE), respectively. RFNN has a longer computation time of 44,301 seconds for a data set with 14,182 data. The proposed method can be optimized by the technology of parallel computing and can be applied to real-time prediction.
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paper_title: Bus arrival time prediction using mixed multi-route arrival time data at previous stop

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The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation. ::: First published online 02 May 2017
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paper_title: Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China

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Providing real-time bus arrival information can help to improve the service quality of a transit system and enhance its competitiveness among other transportation modes. Taking the city of Jinan, China, as an example, this study proposes two artificial neural network (ANN) models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data. Also, to contend with the difficulty in capturing the traffic fluctuations over different time periods and account for the impact of signalized intersections, this study also subdivides the collected dataset into a bunch of clusters. Sub-ANN models are then developed for each cluster and further integrated into a hierarchical ANN model. To validate the proposed models, six scenarios with respect to different time periods and route lengths are tested. The results reveal that both proposed ANN models can outperform the Kalman filter model. Particularly, with several selected performance indices, it has been found that the hierarchical ANN model clearly outperforms the other two models in most scenarios.
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paper_title: Traveling time prediction in scheduled transportation with journey segments

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Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a 'source/destination' pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory, works well yet suffers from outliers. To overcome the outliers problem, we use Machine Learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.
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paper_title: Bus arrival time prediction at bus stop with multiple routes

paper_content:

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.
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paper_title: Research of bus arrival prediction model based on GPS and SVM

paper_content:

Bus arrival time prediction is an important part of intelligent transport system, the prediction accuracy directly affects the overall level of the intelligent transport system. This paper mainly studies the hybrid bus arrival time prediction model, which combines the real-time prediction and support vector machine (SVM) model. And in the SVM model, three factors was chosen as reference, which is time, weather, holidays. This paper is based on the analysis of the characteristics of Yuxi intelligent transport system, and choosing the support vector machine (SVM) model as prediction model, which is adaptive, robustness, and fitting small sample prediction. At the same time, using the GPS technology, which solves the problem of the complexity of the road traffic and the singularity of historical data. The model of this paper effectively solves the problem of bus arrival time prediction.
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paper_title: Predicting Bus Real-time Travel Time Basing on both GPS and RFID Data

paper_content:

Instantaneous and accurate prediction of bus arrival time can help improve the quality of bus-arrival-time information service, and attract additional ridership. On the basis of bus running processes, a self-adaptive exponential smoothing algorithm is proposed to predict the bus running speed based on the short-term running speeds of taxis and buses available. And a bus travel time prediction model is proposed, in which the delay caused by the signal control and the acceleration and deceleration are considered. The research results show that there is a significant linear correlation between speeds of buses and taxis on the same link during the same time period, and the overall performance of the radio frequency identification (RFID)-data-based model is superior to that of automatic vehicle location (AVL)- data-only-based model, regardless of whether the traffic congested or not.
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paper_title: Bus Arrival Time Prediction Using RBF Neural Networks Adjusted by Online Data

paper_content:

Abstract This paper proposes an approach combining historical data and real-time situation information to forecast the bus arrival time. The approach includes two phases. Firstly, Radial Basis Function Neural Networks (RBFNN) model is used to learn and approximate the nonlinear relationship in historical data in the first phase. Then, in the second phase, an online oriented method is introduced to adjust to the actual situation, which means to use the practical information to modify the predicted result of RBFNN in the first phase. Afterwards, the system designing outline is given to summarize the structure and components of the system. We did an experimental study on bus route No.21 in Dalian by deploying this system to demonstrate the validity and effectiveness of this approach. In addition, Multiple Linear Regression model, BP Neural Networks and RBFNN without online adjustment are used in contrast. Results show that the approach with RBFNN and online adjustment has a better predicting performance.
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paper_title: Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems

paper_content:

This research effort uses AVL and APC dynamic data to develop a bus travel time model capable of providing real time information on bus arrival times to passengers, via traveler information services and to transit controllers for the application of proactive control strategies. The developed model is based on two Kalman filter algorithms for the prediction of running times and dwell times alternately in an integrated framework. The AVL and APC data used were obtained for a specific bus route in downtown Toronto. The performance of the developed prediction model was tested using "hold out" data and other data from microsimulation representing different scenarios of bus operation along the investigated route using the VISSIM microsimulation software package. The Kalman Filter algorithm outperformed all other developed models in terms of accuracy demonstrating the dynamic ability to update itself based on new data that reflected the changing characteristics of the transit-operating environment. A user-interactive system was developed to provide continuous information on the expected arrival time of buses at downstream stops, hence the expected deviations from schedule. The system enables the user to assess in real time transit stop-based control actions to avoid such deviations before their occurrence hence allowing for proactive control, as opposed to the traditional reactive control, which attempts to recover the schedule after deviations occur
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paper_title: Using automatic passenger counter data in bus arrival time prediction

paper_content:

Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.
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paper_title: Prediction model of bus arrival and departure times using avl and apc data

paper_content:

The emphasis of this research effort was on using automatic vehicle location (AVL) and Automatic Passenger Counter (APC) dynamic data to develop a bus travel time model capable of providing real time information on bus arrival and departure times to passengers and to transit controllers for the application of proactive control strategies. The developed model is comprised of two Kalman filter algorithms for the prediction of running times and dwell times alternately in an integrated framework. The AVL and APC data used were obtained for a specific bus route in downtown Toronto. The performance of the prediction model was tested using "hold out" data and other data from a microsimulation model representing different scenarios of bus operation along the investigated route using the VISSIM microsimulation software package. The Kalman filter-based model outperformed other conventional models in terms of accuracy, demonstrating the dynamic ability to update itself based on new data that reflected the changing characteristics of the transit operating environment.
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paper_title: Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China

paper_content:

Providing real-time bus arrival information can help to improve the service quality of a transit system and enhance its competitiveness among other transportation modes. Taking the city of Jinan, China, as an example, this study proposes two artificial neural network (ANN) models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data. Also, to contend with the difficulty in capturing the traffic fluctuations over different time periods and account for the impact of signalized intersections, this study also subdivides the collected dataset into a bunch of clusters. Sub-ANN models are then developed for each cluster and further integrated into a hierarchical ANN model. To validate the proposed models, six scenarios with respect to different time periods and route lengths are tested. The results reveal that both proposed ANN models can outperform the Kalman filter model. Particularly, with several selected performance indices, it has been found that the hierarchical ANN model clearly outperforms the other two models in most scenarios.
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paper_title: EasyComeEasyGo: Predicting Bus Arrival Time with Smart Phone

paper_content:

Public transport, especially the bus transport, has been well developed in most cities. Thus the technique of bus arrival time prediction has become a research hotspot nowadays. It is a very important subject to improve the precision and reliability of the bus arrival time prediction, reduce travelers' anxieties and waiting times at bus stop. In this paper, we propose EasyComeEasyGo(ECEG), a novel bus arrival time prediction system based on smart phone for passengers who are traveling with bus in real-life scenarios. This study presents an algorithm that uses real-time GPS data from field and takes delays automatically into account for an accurate prediction of bus arrival time. We develop a prototype system with different types of android-based smart phone and actual GPS data from bus route 26 located in Dalian, China are used as a test bed. We have extensively evaluated the ECEG system with the bus route 26 over a month period. Our results suggest that the proposed system achieves outstanding prediction accuracy and gains most travelers' satisfy.
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paper_title: A Dynamic Bus-Arrival Time Prediction Model Based on APC Data

paper_content:

Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations manage- ment, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of- week, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location informa- tion. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.
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paper_title: Dynamic bus arrival time prediction with artificial neural networks

paper_content:

Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.
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paper_title: Over half of psychology studies fail reproducibility test

paper_content:

Largest replication study to date casts doubt on many published positive results.
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paper_title: Metascience could rescue the ‘replication crisis’

paper_content:

Independent replication of studies before publication may reveal sources of unreliable results, says Jonathan W. Schooler.
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paper_title: Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

paper_content:

Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. [J Med Internet Res 2016;18(12):e323]
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paper_title: Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

paper_content:

Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research ques...
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Title: Survey of ETA Prediction Methods in Public Transport Networks

Section 1: Introduction
Description 1: This section introduces the context of the study, highlighting the importance of public transport, the challenges with ETA prediction, and the impact of accurate ETA predictions on congestion and passenger satisfaction.

Section 2: AVL as Sole Data Source
Description 2: This section reviews studies utilizing Automatic Vehicle Location (AVL) data as the primary source for ETA predictions and discusses different algorithms applied, including historical methods, Kalman Filters, and Neural Networks.

Section 3: Trajectory Based Methods
Description 3: This section examines methods using historical bus trajectories to predict ETAs, comparing current bus routes with past ones using various algorithms like k-Nearest Neighbour and kernel regression.

Section 4: AVL and Headway Information
Description 4: This section explores prediction methods that incorporate AVL data along with headway information (the distance or time to preceding buses) to improve accuracy, evaluating techniques such as Support Vector Machines and Neural Networks.

Section 5: AVL and External Data
Description 5: This section looks at the inclusion of external factors like weather and traffic conditions in ETA predictions, analyzing studies that combine these data with AVL data and the effect on prediction accuracy.

Section 6: AVL and Passenger Data
Description 6: This section discusses the impact of passenger data, such as boarding and alighting numbers, on ETA predictions, reviewing studies that utilize these data alongside AVL information in different modeling techniques.

Section 7: AVL, Passenger, and External Data
Description 7: This section addresses methods that integrate AVL, passenger, and external data for comprehensive ETA predictions, focusing on hybrid models combining various data inputs to enhance prediction reliability.

Section 8: Discussion
Description 8: This section analyzes the findings from the review, highlighting flaws in current research, issues with comparability, the need for consistent reporting standards, and the impact of different data inputs on prediction accuracy.

Section 9: Conclusion
Description 9: This section concludes the review, summarizing the main findings, emphasizing the dominance and challenges of Neural Networks in ETA predictions, and calling for standardized datasets and reporting accuracy for better comparability in future research.