Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data

The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.

Factors Associated with Cessation Activities amongst a Multiethnic Sample of Transit Workers

Introduction: Transit workers are a blue-collar occupational group with elevated rates of smoking despite access to free or low-cost cessation services available through their health insurance as a union-negotiated employee benefit. Little is known about the influences on cessation participation in this workforce.Aims: The purpose of this study is to analyse the factors associated with past-year cessation activities amongst a multiethnic sample of transit workers.Methods: Cross-sectional tobacco surveys were completed by 935 workers at an Oakland, California, USA-based public transit agency. Data from 190 current smokers (68% African American; 46% female) were analysed. Adjusted odds ratios were calculated to identify factors associated with past-year cessation activity.Results: Approximately 55% of smokers stopped smoking for one day or longer during the past year in order to quit. Nearly half reported that the most common barrier to quitting was, ‘Not mentally ready to quit because I like smoking’. Workers in the contemplation/precontemplation stage for intention to quit were less likely to have engaged in cessation activities than those in the action/preparation stage (AOR = 0.34). Frequency of coworker encouragement for quitting was positively associated with past-year cessation activities (AOR = 3.25). Frequency of insomnia symptoms was negatively associated with cessation activity participation (AOR = 0.34).Conclusions: Most transit workers who smoke made a past-year quit attempt. Gaining insight into factors associated with participation in cessation activities can aid worksite efforts to promote cessation and reduce tobacco-related disparities.

Assisting a Public Transit Agency with Planning of Bus Schedules using Computer Simulation: A Collaborative Project between Cornell University and Tompkins Consolidated Area Transit (TCAT) in Ithaca, NY

In this paper we present results from a service learning project carried out by four Master of Engineering students in the School of Operations Research and Industrial Engineering at Cornell University during the 2003-2004 academic year, on behalf of the local transit agency, TCAT. The project participants developed a simulation model to evaluate schedule changes and applied it to a proposed shortening of the time between bus arrivals, or “headway”, on TCAT’s Route 81 circulator bus that serves the Cornell campus in Ithaca, NY. The model was developed by adapting a commercial simulation software package called ProModel that is usually used to simulate the layout of manufacturing facilities. Use of the simulation model helped TCAT planners better understand the implications of the proposed schedule change to segment-by-segment passenger counts at stops and on-board vehicles. Based on our experience, we discuss a number of issues both with the development of simulation models for transit operations and the practice of collaboration between university students and faculty and public transit agencies.