Abstract
A web-based intercept survey was designed and implemented in order to capture the response of transit riders in the Chicago metropolitan area to a variety of service disruptions. Current transit riders were intercepted in the field from November 2017 through January 2018, according to a sampling plan based on local ridership information, in order to gain a representative sample for analysis. Each participant completed a questionnaire regarding the intercepted trip, along with demographic and travel experience information. The survey included a series of stated-preference responses where the current trip is randomly disrupted and alternative travel modes are proposed with service characteristics randomly altered from a baseline scenario. This was designed to understand individual trade-offs between various mode alternatives and travel plan modification strategies under a variety of scenarios. Altogether, 659 transit riders gave responses to 2626 different disruption scenarios. In general, a plurality of riders (49%) choose to continue using transit, either waiting for service restoration or using agency-provided shuttle service, although at a decreasing rate as the travel delay increases. Fewer riders, approximately 15%, choose to alter their activity patterns altogether, while 26% would alter their travel to use either a taxi or an alternative transportation network company (TNC). Having a more detailed understanding of the behavior of riders under various disruption scenarios should allow transit agencies to better prepare for service recovery and restoration after and during local disruptions.
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References
Bachok S (2008) Estimating, calibrating and validating demand for feeder services during incident detections. In: Presented at the 30th conference of Australian institutes of transport research, Perth, Australia, p 16
Bai Y, Kattan L (2014) Modeling riders’ behavioral responses to real-time information at light rail transit stations. Transp Res Rec J Transp Res Board 2412:82–92. https://doi.org/10.3141/2412-10
Barnett A (1974) On controlling randomness in transit operations. Transp Sci 8:102–116. https://doi.org/10.1287/trsc.8.2.102
Bender M, Büttner S, Krumke SO (2013) Online delay management on a single train line: beyond competitive analysis. Public Transp 5:243–266. https://doi.org/10.1007/s12469-013-0070-z
Cacchiani V, Huisman D, Kidd M, Kroon L, Toth P, Veelenturf L, Wagenaar J (2014) An overview of recovery models and algorithms for real-time railway rescheduling. Transp Res Part B Methodol 63:15–37. https://doi.org/10.1016/j.trb.2014.01.009
Cadarso L, Marín Á, Maróti G (2013) Recovery of disruptions in rapid transit networks. Transp Res Part E Logist Transp Rev 53:15–33. https://doi.org/10.1016/j.tre.2013.01.013
Candelieri A, Galuzzi BG, Giordani I, Archetti F (2019) Vulnerability of public transportation networks against directed attacks and cascading failures. Public Transp. 11:27–49. https://doi.org/10.1007/s12469-018-00193-7
Carosi S, Gualandi S, Malucelli F, Tresoldi E (2015) Delay management in public transportation: service regularity issues and crew re-scheduling. In: Transp. Res. Procedia, 18th Euro Working Group on Transportation, EWGT 2015, Delft, The Netherlands, vol 10, 483–492. https://doi.org/10.1016/j.trpro.2015.09.002
CMAP (2019) Travel Tracker Survey [WWW Document]. CMAP. URL www.cmap.illinois.gov/data/transportation/travel-tracker-survey
Currie G, Muir C (2017) Understanding passenger perceptions and behaviors during unplanned rail disruptions. In: Transportation Research Procedia. Presented at the World Conference on Transport Research—WCTR, Shanghai, China, pp 4392–4402. https://doi.org/10.1016/j.trpro.2017.05.322
Duarte A, Garcia C, Giannarakis G, Limão S, Polydoropoulou A, Litinas N (2010) New approaches in transportation planning: happiness and transport economics. NETNOMICS Econ Res Electron Netw 11:5–32. https://doi.org/10.1007/s11066-009-9037-2
Eberlein XJ, Wilson NHM, Barnhart C, Bernstein D (1998) The real-time deadheading problem in transit operations control. Transp Res Part B Methodol 32:77–100. https://doi.org/10.1016/S0191-2615(97)00013-1
Eberlein XJ, Wilson NHM, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35:1–18. https://doi.org/10.1287/trsc.35.1.1.10143
Fukasawa N, Yamauchi K, Murakoshi A, Fujinami K, Tatsui D (2012) Provision of forecast train information and consequential impact on decision making for train-choice. Q Rep Railw Tech Res Inst 53:141–147
Ghaemi N, Cats O, Goverde RMP (2017) Railway disruption management challenges and possible solution directions. Public Transp 9:343–364. https://doi.org/10.1007/s12469-017-0157-z
Hua W, Ong GP (2018) Effect of information contagion during train service disruption for an integrated rail-bus transit system. Public Transp 10:571–594. https://doi.org/10.1007/s12469-018-0192-4
Jin JG, Tang LC, Sun L, Lee D-H (2014) Enhancing metro network resilience via localized integration with bus services. Transp Res Part E Logist Transp Rev 63:17–30. https://doi.org/10.1016/j.tre.2014.01.002
Kepaptsoglou K, Karlaftis MG (2009) The bus bridging problem in metro operations: conceptual framework, models and algorithms. Public Transp 1:275–297. https://doi.org/10.1007/s12469-010-0017-6
Kiefer A, Kritzinger S, Doerner KF (2016) Disruption management for the Viennese public transport provider. Public Transp 8:161–183. https://doi.org/10.1007/s12469-016-0123-1
Krueger R, Rashidi TH, Auld J (2019) Preferences for travel-based multitasking: evidence from a survey among public transit users in the Chicago metropolitan area. In: Presented at the 98th Annual Meeting of the Transportation Research Board
Lin T, Shalaby A, Miller E (2016) Transit user behaviour in response to service disruption: state of knowledge. In: Presented at the Canadian Transportation Research Forum 51st Annual Conference—North American Transport Challenges in an Era of Change//Les défis des transports en Amérique du Nord à une aire de changement Toronto, Ontario
Lin T, Srikukenthiran S, Miller EJ, Shalaby A (2018) Econometric analysis of subway user mode choice in response to unplanned subway disruptions. In: Presented at the transportation research board 97th annual meeting transportation research board
Malucelli F, Tresoldi E (2019) Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study. Public Transp. 11:1–25. https://doi.org/10.1007/s12469-019-00196-y
Murray-Tuite P, Wernstedt K, Yin W (2014) Behavioral shifts after a fatal rapid transit accident: a multinomial logit model. Transp Res Part F Traffic Psychol Behav 24:218–230. https://doi.org/10.1016/j.trf.2014.04.014
Pnevmatikou AM, Karlaftis MG, Kepaptsoglou K (2015) Metro service disruptions: how do people choose to travel? Transportation 42:933–949. https://doi.org/10.1007/s11116-015-9656-4
Rahimi E, Shamshiripour A, Shabanpour R, Mohammadian (K) A, Auld J (2020) Analysis of transit users’ response behavior in case of unplanned service disruptions. Transp Res Rec 2674(3):258–271. https://doi.org/10.1177/0361198120911921
Rubin GJ, Brewin CR, Greenberg N, Hughes JH, Simpson J, Wessely S (2007) Enduring consequences of terrorism: 7-month follow-up survey of reactions to the bombings in London on 7 July 2005. Br J Psychiatry J Ment Sci 190:350–356. https://doi.org/10.1192/bjp.bp.106.029785
Saxena N, Rashidi TH, Auld JA (2019) Comparing commuters’ willingness to pay under transit disruptions: delayed vs. canceled services. In: Presented at the 98th Annual Meeting of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C
Shen S, Wilson NHM (2001) An optimal integrated real-time disruption control model for rail transit systems. In: Voß S, Daduna JR (eds) Computer-aided scheduling of public transport Lecture notes in economics and mathematical systems. Springer, Berlin, Heidelberg, pp 335–363. https://doi.org/10.1007/978-3-642-56423-9_19
Teng J, Liu W-R (2015) Development of a behavior-based passenger flow assignment model for urban rail transit in section interruption circumstance. Urban Rail Transit 1:35–46. https://doi.org/10.1007/s40864-015-0002-0
Tsuchiya R, Sugiyama Y, Arisawa R (2007) A route choice support system for use during disrupted train operation. Jpn Railw Eng 47(2):16–18
van Exel NJA, Rietveld P (2009) When strike comes to town…anticipated and actual behavioural reactions to a one-day, pre-announced, complete rail strike in the Netherlands. Transp Res Part A Policy Pract 43:526–535. https://doi.org/10.1016/j.tra.2009.01.003
Wallis Consulting Group (2012) Public transport customer satisfaction monitor: summary report of all modes, January to March 2012. Wallis Consulting Group
Xing Y, Lu J, Chen S, Dissanayake S (2017) Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro. Public Transp 9:501–525. https://doi.org/10.1007/s12469-017-0170-2
Yap M, Nijenstein S, van Oort N (2018) Improving predictions of public transport usage during disturbances based on smart card data. Transp Policy 61:84–95. https://doi.org/10.1016/j.tranpol.2017.10.010
Acknowledgements
This work was performed under a grant from the Federal Transit Administration awarded to the University of Chicago in December 2016 (IL-26-7015-01—Coordinated Transit Response Planning). We would like to acknowledge Pace and Metra as representative stakeholders for Chicago’s transit operators, providing much of their operating experience and rich data resources to the project. In addition, the CTA has generously provided access to their own data sources and has supported the team by providing access to their facilities for the survey and has reviewed the survey design during the planning stage. We also thank the Illinois Department of Transportation for their input, as well as other municipal, local, regional, and national stakeholders for their willingness to participate.
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Auld, J., Ley, H., Verbas, O. et al. A stated-preference intercept survey of transit-rider response to service disruptions. Public Transp 12, 557–585 (2020). https://doi.org/10.1007/s12469-020-00243-z
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DOI: https://doi.org/10.1007/s12469-020-00243-z