01626424

Component

https://doi.org/10.3141/2652-14

Metrolinx, the regional transportation agency tasked with improving the coordination and integration of all transportation modes in the Greater Toronto and Hamilton Area, has developed an exploratory method for analyzing the effects of new fare structures that integrate the fare systems of multiple transit service providers in the region. The method uses a data set of all weekday trips made in the region segmented by modes used and origin–destination information. A formula derived from the mode choice modeling theory is used to obtain fare elasticity based on unit cost, mode share, and time of day. The distribution of elasticities produced is then calibrated according to a literature review of fare elasticities, and in the future, it will be done according to local market research. The result is a spreadsheet-based tool that provides analysts with an ability to test more complex changes to fare systems, including testing fare integration between agencies and introducing fares by distance, mode, time of day, or a combination of those features. Exploratory in nature, the method is not a replacement for comprehensive market research or fare pilots. However, it addresses the shortcomings of traditional fare analyses that use only aggregate elasticities for diverse market segments by better reflecting the spectrum of transit user sensitivities associated with specific travel characteristics. Furthermore, it provides analysts with a straightforward tool to test the effects of complex fare structures more commonly used in Europe and Asia enabled by smart card and open payment technology on ridership, revenue, emissions, and social equity.

01628042

17-06281

English

pp 124–134

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309441933

Digital/other

Figures (4) ; Maps; References (29) ; Tables (2)

Case studies; Elasticity (Economics); Fares; Metropolitan areas; Public transit; Spreadsheets

Metrolinx

Fare structure

Greater Toronto and Hamilton Area (GTHA); Toronto (Canada)

Economics; Finance; Operations and Traffic Management; Public Transportation

TRIS, TRB, ATRI

Dec 8 2016 12:33PM

• Applications of Inferred Origins, Destinations, and Interchanges in Bus Service Planning
• Constructing Transit Origin–Destination Matrices with Spatial Clustering
• Estimating Fare Noninteraction and Evasion with Disaggregate Fare Transaction Data
• Estimation of Platform Waiting Time Distribution Considering Service Reliability Based on Smart Card Data and Performance Reports
• Fare System of Urban Public Transportation Services in Davao City, Philippines
• Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach
• Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data
• Modeling the Impact of Transit Fare Change on Passengers’ Accessibility
• Perceived Reality: Understanding the Relationship Between Customer Perceptions and Operational Characteristics
• Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data
• Statistical Inference of Transit Passenger Boarding Strategies from Farecard Data
• The Youth Pass: A Study of the Conflicts Between Ideal and Practical Research Design in a Pilot Social Program
• Transit Trip Itinerary Inference with GTFS and Smartphone Data