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Estimating multimodal transit ridership with a varying fare structure

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  • Gkritza, Konstantina
  • Karlaftis, Matthew G.
  • Mannering, Fred L.

Abstract

This paper studies public transport demand by estimating a system of equations for multimodal transit systems where different modes may act competitively or cooperatively. Using data from Athens, Greece, we explicitly correct for higher-order serial correlation in the error terms and investigate two, largely overlooked, questions in the transit literature; first, whether a varying fare structure in a multimodal transit system affects demand and, second, what the determinants of ticket versus travelcard sales may be. Model estimation results suggest that the effect of fare type on ridership levels in a multimodal system varies by mode and by relative ticket to travelcard prices. Further, regardless of competition or cooperation between modes, fare increases will have limited effects on ridership, but the magnitude of these effects does depend on the relative ticket to travelcard prices. Finally, incorrectly assuming serial independence for the error terms during model estimation could yield upward or downward biased parameters and hence result in incorrect inferences and policy recommendations.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:2:p:148-160
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    10. Zi-jia Wang & Feng Chen & Bo Wang & Jian-ling Huang, 2018. "Passengers’ response to transit fare change: an ex post appraisal using smart card data," Transportation, Springer, vol. 45(5), pages 1559-1578, September.
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    13. Jiechao Zhang & Xuedong Yan & Meiwu An & Li Sun, 2017. "The Impact of Beijing Subway’s New Fare Policy on Riders’ Attitude, Travel Pattern and Demand," Sustainability, MDPI, vol. 9(5), pages 1-21, April.
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