Mining Smart Card Data for Travelers' Mini Activities

TL;DR

This paper proposes a novel method to extract and incorporate mini activities from smart card data into transit trip models, significantly reducing the mismatch between simulated and observed passenger trips.

Contribution

It introduces a new technique to mine mini activities from smart card data and integrate them into travel demand models using Markov chains, improving accuracy.

Findings

Significant reduction in trip mismatch in experiments

Effective extraction of mini activities from smart card data

Improved realism in transit trip simulations

Abstract

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.