Transforming Bus Service Planning Using Integrated Electronic Data Sources at NYC Transit

Hanft, Jeffrey; Iyer, Shrisan; Levine, Brian; Reddy, A. Venugopal

doi:10.5038/2375-0901.19.2.6

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…They test a study case in San Francisco City. Regarding routing [140], planning [141, 142, 144] and simulation [143], we mention different studies of each topic: Cerotti et al in [140] present an optimisation system by exploiting feedback‐driven patterns in a distributed‐opportunistic way. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modelled and evaluated by Markovian agents.…”

Section: Applications and Related Projectsmentioning

confidence: 99%

“…Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modelled and evaluated by Markovian agents. Regarding planning, several works relate to general traffic planning, as in [142] (using IoT), [141] (for bus planning) and [144] (for pedestrian planning). As for simulation, Shi et al in [143] reflect the benefits of Big Data sources when used to calibrate and tune microscopic traffic simulation and gain enhanced insights on the safety of the road network. Emergency and incident management, which consists of planned and coordinated multi‐disciplinary tasks jointly aimed at detecting, responding to, and clearing traffic incidents so that traffic flow may be restored as safely and quickly as possible.…”

Section: Applications and Related Projectsmentioning

confidence: 99%

See 1 more Smart Citation

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

116

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Section: Applications and Related Projectsmentioning

confidence: 99%

Section: Applications and Related Projectsmentioning

confidence: 99%

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

116

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“…Zeng et al describe the development of software for New York City Transit to determine boarding and alighting locations for bus trips (20); that information is now in use for scheduling. The software forms the basis for the applications described by Hanft et al, including an assessment of where to split a long route and a neighborhood study that led to stop changes and low-cost reroutes (21).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Vanderwaart

Attanucci

Salvucci

2017

Transportation Research Record: Journal of the Transportation R

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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.

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“…The open source code for their tool is available at Github (see https ://githu b.com/conve yal). Hanft et al (2016) points out that most transit agencies lack the resources to develop comprehensive ridership data and the complex, transit demand models, similar to those used by New York City Transit (NYCT). Understanding the data ecosystem within a transit agency is critical to employing the most efficient and effective approach to forecasting transit ridership.…”

Section: Introductionmentioning

confidence: 99%

Forecasting bus ridership using a “Blended Approach”

2019

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As sources of "Big Data" continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used? This research examines a "blended data" approach, using a web-based, open source platform to assist transit agencies to forecast bus ridership. The platform is capable of incorporating new Big Data sources and traditional data sources, using modern processing techniques and tools, particularly Application Programming Interfaces (APIs). This research demonstrates the use of APIs in a transit demand methodology that yields a robust model for bus ridership. The approach uses the Census Transportation Planning Products data, modified with American Community Survey data, to generate origin-destination tables for bus trips in a designated market area. Microsimulation models us a transit scheduling specification (General Transit Feed Specification) and an open source routing engine (OpenTripPlanner). Local farebox data validates the microsimulation models. Analyses of model output and farebox data for the Atlantic City transit market area, and a scenario analysis of service reduction in the Princeton/Trenton transit market area, illustrate the use a "blended approach" for bus ridership forecasting.

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Transforming Bus Service Planning Using Integrated Electronic Data Sources at NYC Transit

Abstract: Abstract

Cited by 14 publications

References 6 publications

Big Data for transportation and mobility: recent advances, trends and challenges

Big Data for transportation and mobility: recent advances, trends and challenges

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

Forecasting bus ridership using a “Blended Approach”

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