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A dynamic panel analysis of urban metro demand

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  • Graham, Daniel J.
  • Crotte, Amado
  • Anderson, Richard J.

Abstract

A dynamic panel model is used to estimate the effect that fares, income and quality of service have on demand for a sample of 22 urban metros. The estimated price elasticity is -0.05 in the short run and -0.33 in the long run. The estimated long run income elasticity is small but positive (0.18), indicating that metros are perceived as normal goods. The quality of service elasticities are positive and substantially higher than the absolute value of fare elasticities. The implication is that quality of service improvements, rather than fare reductions, may be more effective in increasing metro patronage.

Suggested Citation

  • Graham, Daniel J. & Crotte, Amado & Anderson, Richard J., 2009. "A dynamic panel analysis of urban metro demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 787-794, September.
    • Handle: RePEc:eee:transe:v:45:y:2009:i:5:p:787-794
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    Citations

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    Cited by:

    1. Jaewon Lim & DooHwan Won, 2019. "Impact of CARB’s Tailpipe Emission Standard Policy on CO 2 Reduction among the U.S. States," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    2. Gomez, Juan & Vassallo, José Manuel, 2015. "Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 282-297.
    3. Guzman, Luis A. & Beltran, Carlos & Bonilla, Jorge & Gomez Cardona, Santiago, 2021. "BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 335-348.
    4. Xue-ting Jiang & Min Su & Rongrong Li, 2018. "Investigating the Factors Influencing the Decoupling of Transport-Related Carbon Emissions from Turnover Volume in China," Sustainability, MDPI, vol. 10(9), pages 1-17, August.
    5. Xuto, Praj & Bansal, Prateek & Anderson, Richard J. & Graham, Daniel J. & Hörcher, Daniel & Barron, Alexander, 2023. "Examining the impacts of capital investment in London’s Underground: A long-term analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    6. Steurer, Nora & Bonilla, David, 2016. "Building sustainable transport futures for the Mexico City Metropolitan Area," Transport Policy, Elsevier, vol. 52(C), pages 121-133.
    7. 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.
    8. Gkritza, Konstantina & Karlaftis, Matthew G. & Mannering, Fred L., 2011. "Estimating multimodal transit ridership with a varying fare structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 148-160, February.
    9. Juan Gomez & José Manuel Vassallo & Israel Herraiz, 2016. "Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain," Transportation, Springer, vol. 43(4), pages 677-703, July.
    10. Yinghan Zhu & Liudan Jiao & Yu Zhang & Ya Wu & Xiaosen Huo, 2021. "Sustainable Development of Urban Metro System: Perspective of Coordination between Supply and Demand," IJERPH, MDPI, vol. 18(19), pages 1-24, September.
    11. Konečný Vladimír & Berežný Róbert & Petro František & Trnovcová Martina, 2017. "Research on Demand for Bus Transport and Transport Habits of High School Students in Žilina Region," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 8(2), pages 47-58, November.
    12. Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    13. Chi-Hong (Patrick) Tsai & Corinne Mulley & Geoffrey Clifton, 2014. "A Review of Pseudo Panel Data Approach in Estimating Short-run and Long-run Public Transport Demand Elasticities," Transport Reviews, Taylor & Francis Journals, vol. 34(1), pages 102-121, January.
    14. Michaelides, Panayotis G. & Konstantakis, Konstantinos N. & Milioti, Christina & Karlaftis, Matthew G., 2015. "Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 1-18.
    15. Zhang, Chuanguo & Nian, Jiang, 2013. "Panel estimation for transport sector CO2 emissions and its affecting factors: A regional analysis in China," Energy Policy, Elsevier, vol. 63(C), pages 918-926.
    16. Juan Gomez & José Manuel Vassallo, 2020. "Has heavy vehicle tolling in Europe been effective in reducing road freight transport and promoting modal shift?," Transportation, Springer, vol. 47(2), pages 865-892, April.

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