The Potential of Wi-Fi Data to Estimate Bus Passenger Mobility

Abstract

Last decades have been marked by several socio-economic transformations which have a strong impact on individual mobility behaviors. Using technologies such as Wi-Fi allows to gather passive mobility data, useful for ensuring the sustainable development of transport infrastructures. However, some work still needs to be done to address the limits of Wi-Fi sensors. Our research presents interesting solutions for sorting the transmitted signals and estimating Origin-Destination matrices. With a partitioning algorithm, it is possible to automatically distinguish passengers to get transit ridership flow and O-D matrices. The originality of the paper consists in comparing the results with those of other data sources, and in proposing a methodology that can be reproduced. The findings show that the algorithm is efficient and transferable. They provide concrete and replicable solutions to transport operators for understanding travel demand and managing the quality of service.