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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.
The proposed method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). The method is compared to previous methods and shows improvement in the number of inferred cases.
Inferred boarding and alighting results are used to develop a demand matrix and are visualized to study route ridership and geographical pattern of trips. On the individual level, travel habits of users from multiple days is studied to develop users clusters with similar regularity patterns.
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.
This 60-minute seminar is eligible for 1 hour of professional development credit for AICP (see our provider summary). We can provide an electronic attendance certificate for other types of certification maintenance.
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