LAUSR

Origin-destination (OD) matrices provide significant information to optimize the operation of Public Transportation System and design its evolution. The survey-based approaches to collecting data were overcome by technology providing passengers’ data. Check-In/Check-Out methods generate complete information, but obstructing the alighting and impacting safety. Check-In/Not-Check-Out systems do not detect the destination of passengers, resulting in poor estimates. Be-In/Be-Out (BIBO) approaches seem optimal. We present a complete BIBO system that uses Radio Frequency Identification for passenger monitoring. We first formalize the mathematical framework of the system and propose the set of algorithms that calculate the individual journey of each passenger, the corresponding OD matrices, and a set of operation vectors revealing behaviors at each stop and stretch. We describe a novel visualization tool of OD matrices based on bipartite graphs. We carried out two experiments on a bus in real operation. The obtained results show very high performance metrics. We achieve individual journey estimation rates of 94.7% and 87.9% and error rates of 0.0% and 10.5% and high determination coefficients for OD matrices and operation vectors. Consequently, we can state that our system is capable of producing reliable OD matrices representing the detailed mobility of the Public Transportation Services.