Social-Transportation Analytic Toolbox (STAT) for Transit Networks

Xiaoyue Cathy Liu, University of Utah



The new Social-Transportation Analytic Toolbox (STAT) for Transit Networks, developed by NITC researchers in a multi-university collaboration, is a dynamic platform that combines Twitter, general transit feed specification (GTFS), and census transportation planning products (CTPP)—in this case,  job density data—to help agencies evaluate overall system performance and identify connectivity gaps. It can also act as a decision support tool for recommending service improvements. The STAT is an open-source, publicly accessible toolbox with three components: 
1. Temporal distribution of transit stops’ average travel times, 
2. Transit stop positioning in Google Maps with geomapped tweets around that stop, and 
3. Overall transit access visualization at the TAZ (traffic analysis zone) level.

This toolbox is novel and essential to transit agencies in two aspects. First, it enables the integration, analysis and visualization of two major new open transportation data sources—social media and GTFS data—to support transit decision making. Second, it allows transit agencies to evaluate service network efficiency and access equity of transit systems in a cohesive manner, and identify areas for improvement to better achieve these multi-dimensional objectives. Two transit agencies, the Utah Transit Authority (UTA) and TriMet, worked with the research team to evaluate the usability of the toolbox.

Project Details

Project Type:
Project Status:
End Date:
June 15,2019
UTC Grant Cycle:
NITC 16 Initial Projects
UTC Funding:

Other Products

  • Social-Transportation Analytic Toolbox (STAT) for Transit Networks (PRESENTATION)
  • Dai Z, Liu XC, Chen Z, Guo R, Ma X. A predictive headway-based bus-holding strategy with dynamic control point selection: A cooperative game theory approach. Transportation Research Part B: Methodological. 2019 Jul 1;125:29-51. (PUBLICATION)
  • Chen Z, Liu XC, Wei R. Agent-based approach to analyzing the effects of dynamic ridesharing in a multimodal network. Computers, Environment and Urban Systems. 2019 Mar 1;74:126-35. (PUBLICATION)
  • Haghighi, N. N., Liu, X. C., Wei, R., Li, W. W., & Shao, H. (2018). Using Twitter data for transit performance assessment: a framework for evaluating transit riders' opinions about quality of service. Public Transport, 10(2), 363-377. (PUBLICATION)
  • Social-Transportation Analytic Toolbox (STAT) (WEBSITE)