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Transit service performance analysis and bunching detection using automatic passenger counters (APC) and automatic vehicle location (AVL) data

Team: Sean Qian (PI, CMU), Xidong Pi (CMU)
Funding source: Pennsylvania Infrastructure Technology Alliance
Start/End Time:  2014-2015

The essential idea is to fully utilize the big data in public transit to provide travelers fine-grained customizable information regarding transit service performance (efficiency, reliability and quality). By monitoring day-to-day transit service and how users respond to information provision, we can develop a better understanding of travelers’ preferences on efficiency, reliability and quality of transit service, as well as their modal choices. Big data and data-driven behavioral models facilitate agencies’ decision making (such as scheduling). Effective information provision, along with data-driven scheduling, holds great potential to improve the service performance and travelers’ riding experience.

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Publication

  • Xidong Pi, Sean Qian, Jackson Whitmore, Amy Silbermann (2017), “Understanding Transit System Performance Using APC/AVL Data: a Transit Data Analytics Platform With a Case Study for the Pittsburgh Region”, Journal of Public Transportation, accepted and forthcoming. [URL]

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