Resumen:
Urban mobility optimization problem is a key factor to reduce the
energy consumption and to provide a healthier life in the context of Smart cities. This optimization requires the appropriate sensorization of transport infrastructure and means. The success of social innovations and the application of Big Data and Artificial Intelligence techniques and algorithms rely on the availability of fast, complete and reliable data. In the case of buses, these data are provided from Automatic Fare Collection (AFC), Automatic Passengers’ Counting (APC) sensors and mobile devices data. The use of APC is crucial to determine, in real time, occupancy of buses and passengers’ flow related to a given bus in each stop. This information can be used for a demand based dynamic planning of the buses to optimize bus transport and reduce pollution.
This paper describes the work of a bus sensorization using APC in city of Madrid as part of MUSA-I project, a long-term project oriented to the development of smart bus stops and an inclusive, social driven transport system.