Would you switch? Understanding intra-peak demand shifting among rail commuters

Journal of Public Transportation - Tập 25 - Trang 100073 - 2023
Katharina Burger1, Elisa Becker2, Raffaello Rossi3
1University College London, The Bartlett School of Sustainable Construction, 1-19 Torrington Pl, London WC1E 7HB, United Kingdom
2University of Oxford, Nuffield Department of Primary Care Health Sciences, Oxford OX2 6GG, United Kingdom
3University of Bristol, Business School, Howard House, Queen's Ave, Bristol BS8 1SD, United Kingdom

Tài liệu tham khảo

Aarts, 2000, Habits as knowledge structures: automaticity in goal-directed behavior, J. Pers. Soc. Psychol., 78, 53, 10.1037/0022-3514.78.1.53

Aarts, 1997, Habit and information use in travel mode choices, Acta Psychol., 96, 1, 10.1016/S0001-6918(97)00008-5

Basnak, 2022, Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile, Transp. Res Part A Policy Pr., 159, 140, 10.1016/j.tra.2022.03.011

2012, Applying behavioural insights to reduce fraud, error and debt, Cabinet Off. Lond., 185, 186

Ben-Elia, 2009, Carrots versus sticks: rewarding commuters for avoiding the rush-hour—a study of willingness to participate, Transp. Policy, 16, 68, 10.1016/j.tranpol.2009.03.005

Ben-Elia, 2011, Rewarding rush-hour avoidance: a study of commuters’ travel behavior, Transp. Res Part A Policy Pr., 45, 567, 10.1016/j.tra.2011.03.003

Bradley, 1994, Measuring emotion: the self-assessment manikin and the semantic differential, J. Behav. Ther. Exp. Psychiatry, 25, 49, 10.1016/0005-7916(94)90063-9

Chan, 2022, Impacts of the walking environment on mode and departure time shifts in response to travel time change: case study in the multi-layered Hong Kong metropolis, Travel Behav. Soc., 28, 288, 10.1016/j.tbs.2022.04.010

Cheng, 2022, How does interchange affect passengers’ route choices in urban rail transit?–a case study of the Shanghai Metro, Transp. Lett., 14, 416, 10.1080/19427867.2021.1883803

De Palma, 2017, The economics of crowding in rail transit, J. Urban Econ., 101, 106, 10.1016/j.jue.2017.06.003

Drabicki, 2020, Modelling the effects of real-time crowding information in urban public transport systems, Transp. A: Transp. Sci., 17, 675

Drabicki, 2023, Should I stay or should I board? Willingness to wait with real-time crowding information in urban public transport, Res. Transp. Bus. Manag., 47

Fujii, 2001, Changes in drivers’ perceptions and use of public transport during a freeway closure: effects of temporary structural change on cooperation in a real-life social dilemma, Environ. Behav., 33, 796, 10.1177/00139160121973241

Halvorsen, 2016, Reducing subway crowding: analysis of an off-peak discount experiment in Hong Kong, Transp. Res. Rec., 2544, 38, 10.3141/2544-05

Halvorsen, 2019, Demand management of congested public transport systems: a conceptual framework and application using smart card data, Transportation, 1

Han, 2021, The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions, Transp. Res. Part B: Methodol., 152, 205, 10.1016/j.trb.2021.08.006

Haywood, 2017, Crowding in public transport: who cares and why, Transp. Res. Part A Policy Pr., 100, 215, 10.1016/j.tra.2017.04.022

Hörcher, 2017, Crowding cost estimation with large scale smart card and vehicle location data, Transp. Res. Part B: Methodol., 95, 105, 10.1016/j.trb.2016.10.015

Jenelius, 2020, Personalized predictive public transport crowding information with automated data sources, Transp. Res Part C. Emerg. Technol., 117, 10.1016/j.trc.2020.102647

Kim, 2016

Kim, 2017, Route choice stickiness of public transport passengers: measuring habitual bus ridership behaviour using smart card data, Transp. Res Part C. Emerg. Technol., 83, 146, 10.1016/j.trc.2017.08.005

Kumagai, 2020, Do commuters adapt to in-vehicle crowding on trains, Transportation, 1

Li, 2021, Personalized incentive-based peak avoidance and drivers’ travel time-savings, Transp. Policy, 100, 68, 10.1016/j.tranpol.2020.10.008

Lindsey, 2014, Pre-trip information and route-choice decisions with stochastic travel conditions: theory, Transp. Res. Part B: Methodol., 67, 187, 10.1016/j.trb.2014.05.006

Long, 2023, Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens’ smart cards in the UK’s second largest city region, J. Transp. Geogr., 107, 10.1016/j.jtrangeo.2023.103529

Mahudin, 2012, Measuring rail passenger crowding: scale development and psychometric properties, Transp. Res Part F. Traffic Psychol. Behav., 15, 38, 10.1016/j.trf.2011.11.006

Millet, 1997, The effectiveness of alternative preference elicitation methods in the analytic hierarchy process, J. Multi‐Criteria Decis. Anal., 6, 41, 10.1002/(SICI)1099-1360(199701)6:1<41::AID-MCDA122>3.0.CO;2-D

de Palma, 2015, Discomfort in mass transit and its implication for scheduling and pricing, Transp. Res. Part B: Methodol., 71, 1, 10.1016/j.trb.2014.10.001

Qi, 2019, Individual response modes to pre-trip information in congestible networks: laboratory experiment, Transp. A: Transp. Sci., 15, 376

Rapoport, 2018, Strategic interactions in transportation networks, Handb. Behav. Oper., 557, 10.1002/9781119138341.ch16

Rapoport, 2014, Pre-trip information and route-choice decisions with stochastic travel conditions: experiment, Transp. Res. Part B: Methodol., 68, 154, 10.1016/j.trb.2014.06.007

Shelat, 2022, Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands, Transp. Res. Part A Policy Pr., 159, 357, 10.1016/j.tra.2022.03.027

Tang, 2020, Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems: modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems, Transp. Res. Part B: Methodol., 138, 247, 10.1016/j.trb.2020.05.006

Tirachini, 2013, Crowding in public transport systems: effects on users operation and implications for the estimation of demand, Transp. Res. Part A Policy Pr., 53, 36, 10.1016/j.tra.2013.06.005

Wang, 2020, Commuter value perceptions in peak avoidance behavior: an empirical study in the Beijing subway system, Transp. Res Part A Policy Pr., 139, 70, 10.1016/j.tra.2020.07.001

Wang, 2020, Understanding travelers’ mobility decisions in response to customer incentives, Transp. Policy, 10.1016/j.tranpol.2020.03.017

Wardman, 2011, Twenty years of rail crowding valuation studies: evidence and lessons from British experience, Transp. Rev., 31, 379, 10.1080/01441647.2010.519127

Yang, 2022, Competition and coordination in public transport: a mode choice experiment, Transp. Res. Part C. Emerg. Technol., 143, 10.1016/j.trc.2022.103858

Yang, 2022, Experimental study and modeling of departure time choice behavior in the bottleneck model with staggered work hours, Travel Behav. Soc., 27, 79, 10.1016/j.tbs.2021.12.004

Yap, 2018, Crowding valuation in urban tram and bus transportation based on smart card data, Transp. A: Transp. Sci., 16, 23

Yap, 2023, Public transport crowding valuation in a post-pandemic era, Transportation, 10.1007/s11116-023-10420-1

Zhang, 2017, Impact of real-time crowding information: a stockholm metro pilot study, Public Transp., 9, 483, 10.1007/s12469-016-0150-y

Zhang, 2014, How does commuting behavior change due to incentives? An empirical study of the Beijing Subway System, Transp. Res. Part F. Traffic Psychol. Behav., 24, 17, 10.1016/j.trf.2014.02.009

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Journal of Public Transportation

Tập 25

100073

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