Applications of Inferred Origins, Destinations, and Interchanges in Bus Service Planning

Abstract

A growing number of researchers and transit agencies are using fare card and vehicle location data to infer passengers’ origins, destinations, and transfers. A number of researchers have suggested that these new data sets provide valuable information for transit network design, but few concrete applications have been developed to address bus network design and service planning problems. This paper proposes new service planning procedures to aggregate these automated data to examine travel patterns to specific locations of interest to propose needed improvements. The data from existing passengers’ trips are then analyzed to assess the benefits of the proposed service changes. In particular, the number of existing passengers who would likely experience shorter travel times with the service changes is calculated according to the geometry of how a proposed new or extended route intersects with the existing transit network. The results of this analysis provide planners with better information than is currently available to support decisions on how to allocate the scarce resources typically available for service changes. Several case studies from the Massachusetts Bay Transportation Authority are presented to illustrate these analytical techniques.

Applying Spatial Aggregation Methods to Identify Opportunities for New Bus Services in London

“…In networks where smart card usage is prevalent, this provides a comprehensive picture of public transport travel. Vanderwaart et al developed a framework for using complete OD-level journey data for service planning ( 13 ). The framework includes the selection of target locations, evaluation of network and demand characteristics, and proposed service changes.…”

Using General Transit Feed Specification-Ride to Identify Riders’ Use of Transfer Opportunities

“…However, for small- and medium-sized transit agencies, AFC technology can be challenging because such agencies often lack sufficient staff and funding for implementation. Even for transit agencies with such data available, the sophisticated processing and analyses needed to extract useful information are beyond their technical capacity ( 2 ). Typically, such analyses require some form of transfer inference algorithm because even with AFC technology in place, riders may only pay at one end of a trip ( 3 ).…”

Bus Load Inference and Crowding Performance Evaluation through Disaggregate Analysis of Fare Transaction, Vehicle Location, and Passenger Count Data

“…The current state of practice leaves many agencies relying more on passenger complaints than on their own data to address crowding. The increasing availability of automatically collected data, especially from automated fare collection (AFC), automated vehicle location (AVL), and automatic passenger counting (APC) systems, has enabled the development of data-driven approaches to service planning ( 19 ), demand analysis ( 20 – 24 ), and performance measurement ( 25 , 26 ) in public transportation agencies. While data alone seldom provide the information required to solve key transportation problems, the combination of various data sources with mathematical models can ( 27 ).…”