A geospatial workflow for the assessment of public transit system performance using near real‐time data.

This article presents the development of a Geographical Information Systems (GIS) workflow that harvests high‐volume and high‐frequency near real‐time data from a public General Transit Feed Specification (GTFS) and calculates metrics for the assessment of on‐time and route speed performance for a public transit system. The approach is applied to near real‐time and static GTFS data collected over a 9‐month period for the City of Calgary, Alberta, Canada. The workflow uses two Azure Virtual Machines (VMs), one to harvest the data and the other to process observations in parallel using Python and the ArcGIS API libraries. A Web GIS application is described that queries data from MongoDB to visualize the performance results in spatiotemporal form. The purpose of the workflow and Web GIS application is to provide actionable information to transit planners to improve public transportation systems. The data management and analysis workflow is transferable to similar GTFS data from other cities. [ABSTRACT FROM AUTHOR]