Context-Aware Automated Passenger Counting Data Denoising
Noëlie Cherrier Baptiste Rérolle Martin Graive Amir Dib Eglantine Schmitt
A reliable and accurate knowledge of the ridership in public transportation
networks is crucial for public transport operators and public authorities to be
aware of their network's use and optimize transport offering. Several
techniques to estimate ridership exist nowadays, some of them in an automated
manner. Among them, Automatic Passenger Counting (APC) systems detect
passengers entering and leaving the vehicle at each station of its course.
However, data resulting from these systems are often noisy or even biased,
resulting in under or overestimation of onboard occupancy. In this work, we
propose a denoising algorithm for APC data to improve their robustness and ease
their analyzes. The proposed approach consists in a constrained integer linear
optimization, taking advantage of ticketing data and historical ridership data
to further constrain and guide the optimization. The performances are assessed
and compared to other denoising methods on several public transportation
networks in France, to manual counts available on one of these networks, and on
simulated data.