Mining Smart Card Data for Travelers' Mini Activities
This work addresses a specific problem in public transport simulation for urban planners and modelers, offering an incremental improvement by refining trip generation with real-world data.
The paper tackles the mismatch between simulated transit trips and observed ones in public transport modeling by introducing 'mini activities' that travelers perform during trips, which are mined from smart card data. The method integrates these activities using a Markov chain and Monte Carlo Markov Chain algorithm, achieving a significant reduction in mismatch in experiments on Nancy, France passenger data.
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 over-optimistic and do not reflect the real passenger choices. We introduce the notion of mini activities the travelers do during the trips; they can explain the deviation of simulated trips from the observed trips. We propose to mine the smart card data to extract 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 selected characteristics converge to the desired distributions. We test our method in different settings on the passenger trip collection of Nancy, France. We report experimental results demonstrating a very important mismatch reduction.