Mining Smart Card Data for Travelers' Mini Activities

In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modeling process; the trips it generates are overly optimistic and do not reflect the real passenger choices. To explain the deviation of simulated trips from the observed trips, we introduce the notion of mini-activities the travelers do during the trips. We propose to mine the smart card data and identify characteristics that help detect the mini activities. We develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, we build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the trip characteristics converge to the target distributions. We test our method on the trip data set collected in Nancy, France. The evaluation results demonstrate a very important reduction of the trip generation error, and a good capacity to cope with new simulation scenarios.

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