Developing a new job accessibility measurement based on crowdsourced traffic data and GTFS

This research aims to develop a new innovative job accessibility measurement that captures actual travel impedance by automobile and public transit. To illustrate the refined job accessibility measurement, this study empirically measures auto-based job accessibility and transit-based job accessibility of regions in the Chicago Metropolitan Statistical Area. This research uses Google Maps Distance Matrix API to get actual auto travel time. Additionally, to capture numerous factors that may affect transit travel impedance, this research computes in-vehicle travel time, out-vehicle travel time, the number of transfers, and the number of feasible alternative routes of transit trips by using the General Transit Feed Specification (GTFS) dataset. These computed data are used to estimate the utility-based travel impedance functions. Based on the utility-based travel impedance functions estimated by the binary logit choice model, this research refines a job accessibility measurement that better captures actual travel impedance of commuters. The refined job accessibility measurement demonstrates more detailed spatial patterns of job accessibility of workers that were not revealed by conventional job accessibility measurements. The significant contribution of this research is to improve job accessibility measurements that capture actual travel impedance by using crowdsourced real-time traffic data and detailed profiles of transit routes. These improvements are possible thanks to recent advances in GIS and Big Data technologies.

Name thesis:degree_name

M.U.P.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Urban Planning

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Kim, Junghwan

Contributors dc:contributor

• Lee, Bumsoo
• Braun, Lindsay Maurer

• Spatial mismatch, job accessibility, public transit, travel impedance, Google Maps API, General Transit Feed Specification (GTFS)

Statement dc:rights

• © 2018 Junghwan Kim

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101647

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101647