Understanding the determinants of demand for public transport: Evidence from suburban rail operations in five divisions of Indian Railways. (May 2016)

• Record Type: Journal Article
• Authors: Rahman, Syed; Balijepalli, Chandra
• Is Part Of: Transport policy. Volume 48(2016)
• Page Start: 13
• Page End: 22
• Publication Date: 2016-05
• Subjects: Fare elasticity -- Demand for public transport -- Suburban rail operations in India -- Static/dynamic modelling -- Bootstrapping; Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2016.02.006
• Languages: English
• ISSNs: 0967-070X

Abstract

Abstract: This paper analyses suburban rail fare elasticity and compares the results across five suburban divisional operations of the Indian Railways in three cities viz., Chennai, Kolkata and Mumbai. The three cities chosen have a highly varying modal share of public transport trips and thus offer interesting insights into the attitudes of trip makers towards the changes in operational variables such as fares, service levels. This paper contributes towards understanding of the determinants of demand for public transport in a developing country and applies econometric methods involving static and dynamic modelling methodologies. This research addresses the question of smaller sample sizes which constrain the use of standard regression approaches and applies a bootstrapping method which substitutes for traditional assumptions on distributions and asymptotic results. It was found that the suburban rail demand is inelastic to fare which indicates that the revenue would increase with an increase in fare. Finally, the paper illustrates the use of computed elasticities by estimating the demand for suburban rail in Kolkata. Highlights: Reviews the elasticity work from developed and developing countries Computes, compares the suburban rail fare elasticity of five divisional operations in India Applies bootstrap method to arrive at static, dynamic time series and panel data estimations Illustrates the use of elasticities with an example of demand estimation Identifies policy implications for demand estimation, pricing, promoting sustainable transport