Benefits and issues of bus travel time estimation and prediction

Bus travel time estimation and prediction are two important modelling approaches which could facilitate transit users in using and transit providers in managing the public transport network. Bus travel time estimation could assist transit operators in understanding and improving the reliability of their systems and attracting more public transport users. On the other hand, bus travel time prediction is an important component of a traveller information system which could reduce the anxiety and stress for the travellers. This paper provides an insight into the characteristic of bus in traffic and the factors that influence bus travel time. A critical overview of the state-of-the-art in bus travel time estimation and prediction is provided and the needs for research in this important area are highlighted. The possibility of using vehicle identification data (VID) for studying the relationship between bus and cars travel time is also explored.

[1]  Brian L. Smith,et al.  Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[2]  R. Bertini,et al.  Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation , 2004 .

[3]  Thomas Urbanik,et al.  MODEL TO EVALUATE THE IMPACTS OF BUS PRIORITY ON SIGNALIZED INTERSECTIONS , 1994 .

[4]  Mark D. Abkowitz,et al.  FACTORS AFFECTING RUNNING TIME ON TRANSIT ROUTES , 1983 .

[5]  Jiann-Shiou Yang,et al.  Application of the ARIMA Models to Urban Roadway Travel Time Prediction - A Case Study , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[6]  Radosław Bąk,et al.  Simulation model of the bus stop , 2010 .

[7]  D. Dailey,et al.  Preprint Title: an Algorithm for Predicting the Arrrival Time of Mass Transit Vehicles Using Automatic Vehicle Location Data an Algorithm for Predicting the Arrival Time of Mass Transit Vehicles Using Automatic Vehicle Location Data , 2022 .

[8]  Dong Zhang,et al.  Dwell Time Estimation with Consideration of Bus Bunching , 2012 .

[9]  Dongjoo Park,et al.  Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network , 1999 .

[10]  Joshua Michael Pilachowski.  An Approach to Reducing Bus Bunching , 2009 .

[11]  H. Liu,et al.  Travel time prediction for urban networks , 2008 .

[12]  Wei Wang,et al.  ICCTP 2011: Towards Sustainable Transportation Systems , 2011 .

[13]  Michael J Demetsky,et al.  MODELING SCHEDULE DEVIATIONS OF BUSES USING AUTOMATIC VEHICLE-LOCATION DATA AND ARTIFICIAL NEURAL NETWORKS , 1995 .

[14]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[15]  M. Baucus.  Transportation Research Board , 1982 .

[16]  Robert L. Bertini,et al.  Modeling Transit Trip Time using Archived Bus Dispatch System Data , 2004 .

[17]  Ran Hee Jeong.  The prediction of bus arrival time using automatic vehicle location systems data , 2004 .

[18]  Amer Shalaby,et al.  Development, Evaluation, and Selection of Advanced Transit Signal Priority Concept Directions , 2006 .

[19]  Hans van Lint,et al.  Reliable travel time prediction for freeways , 2004 .

[20]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[21]  Gábor Horváth,et al.  CMAC : RECONSIDERING AN OLD NEURAL NETWORK , 2003 .

[22]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[23]  Laurence R. Rilett,et al.  Prediction Model of Bus Arrival Time for Real-Time Applications , 2005 .

[24]  Baozhen Yao,et al.  Bus Arrival Time Prediction Using Support Vector Machines , 2006, J. Intell. Transp. Syst..

[25]  Majid Sarvi,et al.  Journal of Intelligent Transportation Systems: Technology, Planning, and Operations , 2011 .

[26]  Yasuo Asakura,et al.  Empirical Analysis of Travel Time Reliability Measures in Hanshin Expressway Network , 2009, J. Intell. Transp. Syst..

[27]  Herbert S Levinson,et al.  ANALYZING TRANSIT TRAVEL TIME PERFORMANCE , 1983 .

[28]  Yu Bin,et al.  Bus Arrival Time Prediction Using Support Vector Machines , 2006 .

[29]  J. Bates,et al.  The valuation of reliability for personal travel , 2001 .

[30]  Sergio A. Velastin,et al.  A Dynamic Predicting Algorithm for Estimating Bus Arrival Time , 1997 .

[31]  Jonathan M. Bunker,et al.  Modelling the relationships between passenger demand and bus delays at busway stations , 2009 .

[32]  Hong Liang,et al.  Mining travel time from smart card fare data , 2011, Proceedings of the 30th Chinese Control Conference.

[33]  Steve Callas,et al.  DETERMINANTS OF BUS DWELL TIME , 2004 .

[34]  Steven I-Jy Chien,et al.  Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based , 2001 .

[35]  Dong Zhang,et al.  Historical Travel Time Based Bus-Arrival-Time Prediction Model , 2011 .

[36]  Steven I-Jy Chien,et al.  Dynamic Bus Arrival Time Prediction with Artificial Neural Networks , 2002 .

[37]  Ata M. Khan,et al.  Models for Predicting Bus Delays , 1998 .

[38]  R Fernandez,et al.  Data collection and calibration of passenger service time models for the TranSantiago system , 2008 .

[39]  Dongjoo Park,et al.  Dynamic multi-interval bus travel time prediction using bus transit data , 2010 .

[40]  Kay Fitzpatrick,et al.  Effects of Bus Stop Design on Suburban Arterial Operations , 1997 .

[41]  Camille Kamga,et al.  IMPACT OF CONGESTION ON NEW YORK BUS SERVICE , 1997 .

[42]  Nick Tyler,et al.  Effect of Passenger–Bus–Traffic Interactions on Bus Stop Operations , 2005 .

[43]  Martin N. Milkovits.  Modeling the Factors Affecting Bus Stop Dwell Time , 2008 .

[44]  A. Bhaskar.  A methodology (CUPRITE) for urban network travel time estimation by integrating multisource data , 2009 .

[45]  Xiaobo Liu,et al.  A Dynamic Bus‐Arrival Time Prediction Model Based on APC Data , 2004 .

[46]  Ahmed M El-Geneidy,et al.  Estimating Bus Run Times for New Limited-Stop Service Using Archived AVL and APC Data , 2009 .

[47]  Graham Currie,et al.  Active transit signal priority for streetcars: experience in Melbourne and Toronto , 2008 .

[48]  Graham Currie,et al.  Active Transit Signal Priority for Streetcars , 2008 .

[49]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[50]  Partha Chakroborty,et al.  Using Bus Travel Time Data to Estimate Travel Times on Urban Corridors , 2004 .

[51]  Amer Shalaby,et al.  BUS TRAVEL TIME PREDICTION MODEL FOR DYNAMIC OPERATIONS CONTROL AND PASSENGER INFORMATION SYSTEMS , 2003 .

[52]  Rajat Rajbhandari,et al.  Bus arrival time prediction using stochastic time series and Markov chains , 2005 .

[53]  Carlos F. Daganzo.  A DYNAMIC APPROACH TO ELIMINATE BUS BUNCHING , 2009 .

[54]  Prianka N. Seneviratne,et al.  SIMULATION OF FIXED ROUTE BUS TRAVEL TIME , 1988 .

[55]  John Collura,et al.  Improving Transportation Mobility, Safety, and Efficiency: Guidelines forPlanning and Deploying Traffic Signal Priority Strategies , 2008 .

[56]  Ata M. Khan,et al.  Bus running time prediction using a statistical pattern recognition technique , 2010 .

[57]  Steven I-Jy Chien,et al.  ESTIMATION OF BUS ARRIVAL TIMES USING APC DATA , 2004 .

[58]  Albert Gan,et al.  Design of Transit Signal Priority at Intersections with Queue Jumper Lanes , 2008 .

[59]  A. Danaher.  Bus and Rail Transit Preferential Treatments in Mixed Traffic , 2010 .

[60]  Peter G Furth,et al.  Near side, far side, uphill, downhill : Impact of bus stop location on bus delay , 2006 .

[61]  R. J. Baker,et al.  AN OVERVIEW OF TRANSIT SIGNAL PRIORITY , 2002 .

[62]  Suwardo,et al.  ARIMA MODELS FOR BUS TRAVEL TIME PREDICTION , 2010 .

[63]  Jian Zeng,et al.  AN EXPERIMENTAL STUDY ON REAL TIME BUS ARRIVAL TIME PREDICTION WITH GPS DATA , 1999 .