Transfer penalties in multimodal public transport networks

Abstract The disutility of transfers in multimodal public transport goes beyond the additional walking and waiting times. Although the magnitude of this pure transfer penalty has been proven to be an essential element in the structural design of public transport lines, the scarce available research reveals a wide range of values. The aim of this paper is to develop and apply a framework to estimate the value perceived and assigned by commuters to this penalty. This framework includes all the other elements considered by users in the case of a trip involving (potential) transfers, in order to obtain the impact of each one. The framework is based on the discrete choices paradigm and applied to data collected in Madrid, Spain. The results show that the pure transfer penalty is comparable to a 15.2–17.7 equivalent increase in in-vehicle minutes; i.e. longer trips may be preferred to faster alternatives with transfers, even if the additional walking and waiting times are zero. As well as the pure transfer penalty, the model also captures the effects of habit, crowding, walking, waiting and in-vehicle times, information, and the additional effect of intermodality on transfers.

[1]  D. Pfeffermann.  The Role of Sampling Weights when Modeling Survey Data , 1993 .

[2]  Graham Currie,et al.  The demand performance of bus rapid transit , 2005 .

[3]  John Bates,et al.  ECONOMETRIC ISSUES IN STATED PREFERENCE ANALYSIS , 1988 .

[4]  Narcyz Roztocki,et al.  THE USE OF WEB-BASED SURVEYS FOR ACADEMIC RESEARCH IN THE FIELD OF ENGINEERING , 2002 .

[5]  S. Sills,et al.  Innovations in Survey Research , 2002 .

[6]  Alan J Horowitz,et al.  Transfer penalties: Another look at transit riders' reluctance to transfer , 1981 .

[7]  A. Daly,et al.  MODELS USING MIXED STATED-PREFERENCE AND REVEALED-PREFERENCE INFORMATION. , 1991 .

[8]  Joel Huber,et al.  The Importance of Utility Balance in Efficient Choice Designs , 1996 .

[9]  Michel Bierlaire,et al.  Mitigating the impact of errors in travel time reporting on mode choice modelling , 2017 .

[10]  Rocío de Oña,et al.  Urban transport interchanges: A methodology for evaluating perceived quality , 2016 .

[11]  N. Douglas,et al.  Estimating transfer penalties and standardised income values of time by stated preference survey , 2013 .

[12]  M. Birnbaum.  Human research and data collection via the internet. , 2004, Annual review of psychology.

[13]  Cihan Cobanoglu,et al.  A Comparison of Mail, Fax and Web-Based Survey Methods , 2001 .

[14]  Shlomo Bekhor,et al.  Web-based survey design for unravelling semi-compensatory choice in transport and urban planning , 2012 .

[15]  Paula Vicente,et al.  Using Questionnaire Design to Fight Nonresponse Bias in Web Surveys , 2010 .

[16]  Zhan Guo,et al.  Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground , 2011 .

[17]  Hiroyuki Iseki,et al.  Not All Transfers Are Created Equal: Towards a Framework Relating Transfer Connectivity to Travel Behaviour , 2009 .

[18]  Yung-Hsiang Cheng,et al.  Exploring passenger anxiety associated with train travel , 2010 .

[19]  Gi Woong Yun,et al.  Comparative Response to a Survey Executed by Post, E-mail, & Web Form , 2006, J. Comput. Mediat. Commun..

[20]  Ward Vanlaar,et al.  Street racing and stunt driving in Ontario, Canada: Results of a web-based survey of car and racing enthusiasts , 2013 .

[21]  Joel R. Evans,et al.  The value of online surveys , 2005, Internet Res..

[22]  R. M. Nally.  Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000, Biodiversity & Conservation.

[23]  Frederick L. Oswald,et al.  Response Rates for Mixed-Mode Surveys Using Mail and E-mail/Web , 2008 .

[24]  John M. Rose,et al.  Designing efficient stated choice experiments in the presence of reference alternatives , 2008 .

[25]  Yung-Hsiang Cheng,et al.  Exploring the effects of perceived values, free bus transfer, and penalties on intermodal metro–bus transfer users' intention , 2016 .

[26]  Graham Currie,et al.  Bus Network Planning for Transfers and the Network Effect in Melbourne, Australia , 2010 .

[27]  Subeh Chowdhury,et al.  Modelling public-transport users’ behaviour at connection point , 2013 .

[28]  Subeh Chowdhury,et al.  The effects of planned and unplanned transfers on public transport users' perception of transfer routes , 2014 .

[29]  Hani S. Mahmassani,et al.  Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach , 2017 .

[30]  Elisabetta Cherchi,et al.  Workshop Synthesis: Stated Preference Surveys and Experimental Design, an Audit of the Journey so far and Future Research Perspectives , 2015 .

[31]  Tae Youn Jang,et al.  CAUSAL RELATIONSHIP AMONG TRAVEL MODE, ACTIVITY, AND TRAVEL PATTERNS , 2003 .

[32]  D. Hensher.  Stated preference analysis of travel choices: the state of practice , 1994 .

[33]  Sergio R. Jara-Díaz,et al.  Transport Economic Theory , 2007 .

[34]  Sergio R. Jara-Díaz,et al.  Feeder-trunk or direct lines? Economies of density, transfer costs and transit structure in an urban context , 2016 .

[35]  Sergio R. Jara-Díaz,et al.  Optimal public transport networks for a general urban structure , 2016 .

[36]  Juan de Dios Ortúzar,et al.  Subjective valuation of the transit transfer experience: The case of Santiago de Chile , 2013 .

[37]  Jelke Bethlehem,et al.  Indicators for the representativeness of survey response , 2009 .

[38]  Andres Monzon,et al.  Stated preference survey for estimating passenger transfer penalties: design and application to Madrid , 2017 .

[39]  Hjp Harry Timmermans,et al.  Judgments of Travel Experiences, Activity Envelopes, Trip Features and Multi-Tasking: A Panel Effects Regression Model Specification , 2014 .

[40]  Jeffrey M. Wooldridge,et al.  What Are We Weighting For? , 2013, The Journal of Human Resources.

[41]  Michiel C.J. Bliemer,et al.  Constructing Efficient Stated Choice Experimental Designs , 2009 .

[42]  A. Daly,et al.  Use of the logit scaling approach to test for rank-order and fatigue effects in stated preference data , 1994 .

[43]  Thérèse Steenberghen,et al.  Space and time related determinants of public transport use in trip chains , 2006 .

[44]  A. Ceder,et al.  The effects of travel time and cost savings on commuters’ decision to travel on public transport routes involving transfers , 2015 .

[45]  Avishai Ceder,et al.  Transfer Synchronization of Public Transport Networks , 2013 .