Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in Žilina, the Slovak Republic, as a case study

[1]  Luigi dell’Olio,et al.  Optimizing bus-size and headway in transit networks , 2012 .

[2]  Randy B Machemehl,et al.  OPTIMAL TRANSIT ROUTE NETWORK DESIGN PROBLEM: ALGORITHMS, IMPLEMENTATIONS, AND NUMERICAL RESULTS , 2004 .

[3]  Jonathan M. Bunker,et al.  Transit Users’ Route‐Choice Modelling in Transit Assignment: A Review , 2010 .

[4]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[5]  John Douglas Hunt,et al.  A LOGIT MODEL OF PUBLIC TRANSPORT ROUTE CHOICE , 1990 .

[6]  Chandra R. Bhat,et al.  A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models , 2006 .

[7]  Kay W. Axhausen,et al.  State-of-the-Art Estimates of Swiss Value of Travel Time Savings , 2006 .

[8]  Anita Schöbel,et al.  Integrating line planning, timetabling, and vehicle scheduling: a customer-oriented heuristic , 2009, Public Transp..

[9]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

[10]  A. Álvarez,et al.  A computational tool for optimizing the urban public transport: A real application , 2010 .

[11]  L Janosikova,et al.  Design of Urban Public Transport Lines as a Multiple Criteria Optimisation Problem , 2010 .

[12]  Matti Pursula,et al.  Modeling Level-of-Service Factors in Public Transportation Route Choice , 1999 .

[13]  Kay W. Axhausen,et al.  Route, mode and departure time choice behaviour in the presence of mobility pricing , 2007 .

[14]  Wei Wang,et al.  Bus Passenger Origin-Destination Estimation and Related Analyses , 2011 .

[15]  G. C. De Jong,et al.  The Driving Factors of Passenger Transport , 2008 .

[16]  Martin Trépanier,et al.  Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System , 2007, J. Intell. Transp. Syst..

[17]  Martin Grötschel,et al.  A Column-Generation Approach to Line Planning in Public Transport , 2007, Transp. Sci..

[18]  Peter White,et al.  The Potential of Public Transport Smart Card Data , 2005 .

[19]  Laura Eboli,et al.  A Stated Preference Experiment for Measuring Service Quality in Public Transport , 2008 .

[20]  Florian Heiss,et al.  Discrete Choice Methods with Simulation , 2016 .

[21]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .