Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data. (July 2015)

• Record Type: Journal Article
• Authors: Wang, Zi-jia; Li, Xiao-hong; Chen, Feng
• Is Part Of: Transportation research. Volume 77(2015)
• Page Start: 213
• Page End: 224
• Publication Date: 2015-07
• Subjects: Fare change -- Metro -- Network -- Smart card data -- Trip distance -- Demand; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2015.04.018
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: Methodology using transit smart card data on fare change evaluation is formulated. Exhaustive transit smart card data allows assessment of distance based fare change. The short to medium length trips are more sensitive to fare changes. Feasible fare changes according to elasticity variation achieve specific purposes. 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.