Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada

Postsavee Prommaharaj; Santi Phithakkitnukoon; Merkebe Getachew Demissie; Lina Kattan; Carlo Ratti

doi:10.1016/j.heliyon.2020.e03729

Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada

Heliyon. 2020 Apr 14;6(4):e03729. doi: 10.1016/j.heliyon.2020.e03729. eCollection 2020 Apr.

Authors

Postsavee Prommaharaj¹, Santi Phithakkitnukoon¹, Merkebe Getachew Demissie², Lina Kattan², Carlo Ratti³

Affiliations

¹ Department of Computer Engineering, Chiang Mai University, Chiang Mai, Thailand.
² Department of Civil Engineering, University of Calgary, Calgary, Canada.
³ SENSEable City Laboratory, Massachusetts Institute of Technology, MA, USA.

Abstract

Public transportation agencies are one of the industries that generate a large volume of data on a high frequency and velocity basis. The General Transit Feed Specification (GTFS) is one of the datasets these agencies generate and share openly with the public. GTFS feeds contain data for scheduled transit service including stop and route locations, and schedules information. This paper aims to demonstrate the potential of GTFS data, specifically, the paper describes the development of a GTFS data visualization tool that displays spatial and temporal patterns of transit services from which qualitative information and insights can be gained. In this paper, GTFS data from Calgary Transit was used as a case study. Previous studies focused on the development of visualization tools that display transit movement, or static graphical representation of transit operation. However, there is still a need for a dynamic interactive visualization tool that can also measures and displays transit system operation geographically and statistically. This study builds on the previous investigations and further develops a new public transit system operation visualization tool (called PubtraVis) with six visualization modules that reflect on different transit system operational characteristics; mobility, speed, flow, density, headway, and analysis. The user can evaluate two modules side by side for comparative analysis. The analysis module provides an insightful statistical summary and similarity measure and clustering results based on the transit operation characteristics. PubtraVis was tested with real-world users through a user experience study from which it was found to be useful and easy to start using. PubtraVis can be a useful tool to demonstrate the dynamism of transit vehicles from the entire transit network at a glance, and can be used to facilitate communication between transit operators, city authorities, and the general public regarding the public transit planning and operation.

Keywords: Big data; Computer-aided engineering; Data analysis; Data analytics; Data mining; Data visualization; GTFS data; Information management; Information technology; Intelligent transportation systems; Public transit; Smart city; Systems engineering; Urban informatics; Visual analytics.