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Development of origin-destination demand matrices is crucial for transit planning. The development process is facilitated by transit automated data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel stochastic trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specifications (GTFS) data to infer an origin-destination (O-D) matrix.
Alireza Khani is an assistant professor in the department of Civil, Environmental, and Geo- engineering at the University of Minnesota. His research includes transportation network and user behavior modeling with application to transit planning and operations. Transit demand and ridership forecasting, reliability analysis, route choice, and network design are some of the applications of his research. His research on transit systems has been supported by National Science Foundation and transportation agencies such as Minnesota DOT and Metro Transit. Alireza Khani received PhD degree in civil engineering from the University of Arizona. Prior to joining the University of Minnesota, he was a research associate at Network Modeling Center at the University of Texas at Austin.
Local transit, Travel time (Traffic engineering), Transportation -- Planning
Transportation | Transportation Engineering | Urban Studies
Khani, Alireza, "Transit Demand Analysis and User Classification Using Automatic Fare Collection (AFC) Data" (2018). PSU Transportation Seminars. 144.