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Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

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  • Wang, Qing
  • Zhao, Wenjing
  • Ma, Shoufeng
  • Schonfeld, Paul M.
  • Zheng, Yue
  • Xue, Dabin

Abstract

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Suggested Citation

  • Wang, Qing & Zhao, Wenjing & Ma, Shoufeng & Schonfeld, Paul M. & Zheng, Yue & Xue, Dabin, 2023. "Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:transa:v:178:y:2023:i:c:s0965856423002999
    DOI: 10.1016/j.tra.2023.103879
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    as
    1. Nan Yang & Yong Long Lim, 2018. "Temporary Incentives Change Daily Routines: Evidence from a Field Experiment on Singapore’s Subways," Management Science, INFORMS, vol. 64(7), pages 3365-3379, July.
    2. Azari, Kian Ahmadi & Arintono, Sulistyo & Hamid, Hussain & Davoodi, Seyed Rasoul, 2013. "Evaluation of demand for different trip purposes under various congestion pricing scenarios," Journal of Transport Geography, Elsevier, vol. 29(C), pages 43-51.
    3. Rouwendal, Jan & Verhoef, Erik T. & Knockaert, Jasper, 2012. "Give or take? Rewards versus charges for a congested bottleneck," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 166-176.
    4. Karlström, Anders & Franklin, Joel P., 2009. "Behavioral adjustments and equity effects of congestion pricing: Analysis of morning commutes during the Stockholm Trial," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(3), pages 283-296, March.
    5. Coulombel, Nicolas & Monchambert, Guillaume, 2023. "Diseconomies of scale and subsidies in urban public transportation," Journal of Public Economics, Elsevier, vol. 223(C).
    6. Haywood, Luke & Koning, Martin, 2015. "The distribution of crowding costs in public transport: New evidence from Paris," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 182-201.
    7. Luo, Mingjie & Ma, Zhuanglin & Zhao, Wenjing & Enoch, Marcus & I-Jy Chien, Steven, 2022. "An ex-post evaluation of the public acceptance of a license plate-based restriction policy: A case study of Xi’an, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 259-282.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    9. Guo, Qianwen & Sun, Yanshuo & Schonfeld, Paul & Li, Zhongfei, 2021. "Time-dependent transit fare optimization with elastic and spatially distributed demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 353-378.
    10. Mikkel Thorhauge & Akshay Vij & Elisabetta Cherchi, 2021. "Heterogeneity in departure time preferences, flexibility and schedule constraints," Transportation, Springer, vol. 48(4), pages 1865-1893, August.
    11. Saleh, Wafaa & Farrell, Séona, 2005. "Implications of congestion charging for departure time choice: Work and non-work schedule flexibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 773-791.
    12. Börjesson, Maria & Kristoffersson, Ida, 2015. "The Gothenburg congestion charge. Effects, design and politics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 134-146.
    13. Zhao, Wenjing & Ma, Zhuanglin & Yang, Kui & Huang, Helai & Monsuur, Fredrik & Lee, Jaeyoung, 2020. "Impacts of variable message signs on en-route route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 335-349.
    14. Geng, Kexin & Wang, Yacan & Cherchi, Elisabetta & Guarda, Pablo, 2023. "Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    15. Andersson, David & Nässén, Jonas, 2016. "The Gothenburg congestion charge scheme: A pre–post analysis of commuting behavior and travel satisfaction," Journal of Transport Geography, Elsevier, vol. 52(C), pages 82-89.
    16. Coulombel, Nicolas & Munch, Emmanuel & Pivano, Cyril, 2023. "Travel demand management: The solution to public transit congestion? An ex-ante evaluation of staggered work hours schemes for the Paris region," Transport Policy, Elsevier, vol. 137(C), pages 48-66.
    17. Prasanta K. Sahu & Gajanand Sharma & Anirban Guharoy, 2018. "Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai," Public Transport, Springer, vol. 10(3), pages 379-398, December.
    18. Ben Clark & Kiron Chatterjee & Adam Martin & Adrian Davis, 2020. "How commuting affects subjective wellbeing," Transportation, Springer, vol. 47(6), pages 2777-2805, December.
    19. Yang, Hai & Huang, Hai-Jun, 1998. "Principle of marginal-cost pricing: how does it work in a general road network?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(1), pages 45-54, January.
    20. Yang, Hai & Tang, Yili, 2018. "Managing rail transit peak-hour congestion with a fare-reward scheme," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 122-136.
    21. Thommen, Christoph & Hintermann, Beat, 2023. "Price versus Commitment: Managing the demand for off-peak train tickets in a field experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    22. Coulombel, Nicolas, 2018. "Why housing and transport costs should always be considered together: A monocentric analysis of prudential measures in housing access," Transport Policy, Elsevier, vol. 65(C), pages 89-105.
    23. Ben-Elia, Eran & Ettema, Dick, 2009. "Carrots versus sticks: Rewarding commuters for avoiding the rush-hour--a study of willingness to participate," Transport Policy, Elsevier, vol. 16(2), pages 68-76, March.
    24. Hörcher, Daniel & Graham, Daniel J., 2018. "Demand imbalances and multi-period public transport supply," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 106-126.
    25. Mauricio Sillano & Juan de Dios Ortúzar, 2005. "Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence," Environment and Planning A, , vol. 37(3), pages 525-550, March.
    26. Ding, Hongliang & Sze, N.N. & Li, Haojie & Guo, Yanyong, 2021. "Affected area and residual period of London Congestion Charging scheme on road safety," Transport Policy, Elsevier, vol. 100(C), pages 120-128.
    27. Anupriya, & Graham, Daniel J. & Hörcher, Daniel & Anderson, Richard J. & Bansal, Prateek, 2020. "Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 16-34.
    28. Börjesson, Maria & Rubensson, Isak, 2019. "Satisfaction with crowding and other attributes in public transport," Transport Policy, Elsevier, vol. 79(C), pages 213-222.
    29. Thorhauge, Mikkel & Kassahun, Habtamu Tilahun & Cherchi, Elisabetta & Haustein, Sonja, 2020. "Mobility needs, activity patterns and activity flexibility: How subjective and objective constraints influence mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 255-272.
    30. Majumdar, Bandhan Bandhu & Jayakumar, Malavika & Sahu, Prasanta K. & Potoglou, Dimitris, 2021. "Identification of key determinants of travel satisfaction for developing policy instrument to improve quality of life: An analysis of commuting in Delhi," Transport Policy, Elsevier, vol. 110(C), pages 281-292.
    31. Link, Heike, 2015. "Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 96-109.
    32. Li, Haiying & Li, Xian & Xu, Xinyue & Liu, Jun & Ran, Bin, 2018. "Modeling departure time choice of metro passengers with a smart corrected mixed logit model - A case study in Beijing," Transport Policy, Elsevier, vol. 69(C), pages 106-121.
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