Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data |
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| Institution: | 1. School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, PR China;2. Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, No.3 Shangyuancun, Haidian District, Beijing 100044, PR China;3. Department of Civil, Environmental and Geomatic Engineering, University College London, Chadwick Building, Gower Street, London WC1E 6BT, England, United Kingdom;1. Department of Architecture and Urban Studies (DASTU), Politecnico di Milano, Italy;2. Istituto Bruno Leoni (IBL), Italy;3. Department of Architecture and Urban Studies Planning (DASTU), Politecnico di Milano, Italy;1. School of Government, Universidad del Desarrollo, Santiago, Chile;2. School of Industrial Engineering, Universidad Diego Portales, Santiago, Chile;1. Department of Business Administration II, Marketing and Health Care Management, University of Freiburg, Platz der Alten Synagoge, 79085 Freiburg, Germany;2. Department of Business Administration VI, Public and Nonprofit Management, University of Freiburg, Wilhelmstraße 1b, 79085 Freiburg, Germany;1. NS Stations, Stationshal 17, 3511 CE Utrecht, The Netherlands;2. Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;3. Spoorgloren, Schapenhoeve 11, 3992 PL Houten, The Netherlands |
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| Abstract: | Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15 km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue. |
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| Keywords: | Fare change Metro Network Smart card data Trip distance Demand |
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