Contribution to Book
A Big Data Mashing Tool for Measuring Transit System PerformanceSeeing Cities Through Big Data (2016)
This research aims to develop software tools to support the fusion and analysis of large, passively collected data sources for the purpose of measuring and monitoring transit system performance. This study uses San Francisco as a case study, taking advantage of the automated vehicle location (AVL) and automated passenger count (APC) data available on the city transit system. Because the AVL-APC data are only available on a sample of buses, a method is developed to expand the data to be representative of the transit system as a whole. In the expansion process, the General Transit Feed Specification (GTFS) data are used as a measure of the full set of scheduled transit service.
The data mashing tool reports and tracks transit system performance in these key dimensions:
- Service Provided: vehicle trips, service miles;
- Ridership: boardings, passenger miles; passenger hours, wheelchairs served, bicycles served;
- Level-of-service: speed, dwell time, headway, fare, waiting time;
- Reliability: on-time performance, average delay; and
- Crowding: volume-capacity ratio, vehicles over 85 % of capacity, passenger hours over 85 % of capacity.
An important characteristic of this study is that it provides a tool for analyzing the trends over significant time periods—from 2009 through the present. The tool allows data for any two time periods to be queried and compared at the analyst’s request, and puts the focus specifically on the changes that occur in the system, and not just observing current conditions.
- big data,
- performance-based planning,
- automated vehicle location,
- automated passenger count
Publication DateOctober 8, 2016
EditorPiyushimita (Vonu) Thakuriah, Nebiyou Tilahun, Moira Zellner
PublisherSpringer International Publishing
Citation InformationGregory D. Erhardt, Oliver Lock, Elsa Arcaute and Michael Batty. "A Big Data Mashing Tool for Measuring Transit System Performance" 1Seeing Cities Through Big Data (2016) p. 257 - 278
Available at: http://works.bepress.com/gregory-erhardt/17/