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A dynamic panel analysis of urban metro demand

Author

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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. 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.
    2. Steurer, Nora & Bonilla, David, 2016. "Building sustainable transport futures for the Mexico City Metropolitan Area," Transport Policy, Elsevier, vol. 52(C), pages 121-133.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

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