Dynamic Pedestrian Demand Estimation Using Data From Reidentification Sensors: A New Research Challenge

Many cities are currently working on the development of mobility policies aimed at improving the accessibility of transport infrastructures and the intermodality in the citizen’s daily travel. Some of these policies should focus on obtaining a more sustainable modal split distribution in the access to and egress from multimodal transportation hubs. The first step to face this problem should be to obtain a good estimation of this actual modal split. Although different methods are available in the literature, this paper opens a new research challenge proposing to use models calibrated with data obtained from pedestrian reidentification devices as these models allow the direct reconstruction of pedestrian route flows. However, this topic is still a work in progress as the real data required to validate these models should, at the outset, come from reidentification sensors that are under development, and although there are cameras installed in some stations, they are not sensors that are useful for the postprocessing we are looking for. Indeed, among the few models found in the literature dealing with dynamic pedestrian demand estimation, none of them use data from reidentification sensors to reconstruct the OD‐matrix or to establish the pedestrian modal split in the access to and the egress from the station. To fill this gap, this paper sets out to establish the fundamentals of a new dynamic pedestrian estimation model using reidentification data and to propose a genetic algorithm for the determination of the best possible location of PRI sensors in an urban multimodal transportation hub. To do so, a methodology is proposed to use microsimulation tools to obtain realistic data for the development of this model as an alternative to real data until real devices are installed. To demonstrate its applicability, two small fictitious stations and the real case study of Getafe Central station are modeled to explain the method and to generate realistic scenarios that occur daily at train stations to virtually locate pedestrian recognition sensors capable of reidentifying users over several parts of their routes.