Getting People Around After the Trains Stop Running: Transit Propensity Index for Late-Night Service Planning. Issue 1 (2017)

• Record Type: Journal Article
• Authors: Dentel-Post, Colin; Cooper, Drew; Huang, Yeying; Crosley, Steve; Tolleson, Millie; Carraher, Michael
• Is Part Of: Transportation research record. Volume 2649:Issue 1(2017)
• Page Start: 97
• Page End: 105
• Publication Date: 2017
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2649-11
• Languages: English
• ISSNs: 0361-1981

Abstract

Late-night transit service provides an important connection to jobs, entertainment venues, and other destinations in San Francisco, California, and other major cities. In 2016, the San Francisco County Transportation Authority led a comprehensive reexamination of the region's late-night bus network, which provided service between about midnight and 5:00 a.m., while the region's rail services were closed for maintenance. Previous literature established the general characteristics of late-night transit users and trip generators but did not develop and validate the use of a specific tool to plan service. Other researchers also developed transit propensity indexes (TPIs) with the use of demographic data for transit service in general but not specifically for the late-night period. A new approach was used to assess transit demand for late-night work trips by using Census Transportation Planning Package data to identify late-night work trips and combining those trip volumes with additional demographic factors associated with reliance on public transit to develop a late-night TPI. The hypothesis was that high TPI scores were an indicator of areas where late-night transit would attract strong ridership. The research team compared the index results with ridership on existing routes by using a stop-level regression analysis to validate that the TPI is predictive of ridership at a statistically significant level. It was concluded that the TPI together with productivity analysis of existing routes supported identification of potential late-night transit network changes to improve coverage in areas where riders would most need and use the service.