Efficient distributed computation of human mobility aggregates through user mobility profiles

A basic task of urban mobility management is the real-time monitoring of traffic within key areas of the territory, such as main entrances to the city, important attractors and possible bottlenecks. Some of them are well known areas, while while others can appear, disappear or simply change during the year, or even during the week, due for instance to roadworks, accidents and special events (strikes, demonstrations, concerts, new toll road fares). Especially in the latter cases, it would be useful to have a traffic monitoring system able to dynamically adapt to reference areas specified by the user. In this paper we propose and study a solution exploiting on-board location devices in private cars mobility, that continuously trace the position of the vehicle and periodically communicate it to a central station. Such vehicles provide a statistical sample of the whole population, and therefore can be used to compute a summary of the traffic conditions for the mobility manager. However, the large mass of information to be transmitted and processed to achieve that might be too much for a real-time monitoring system, the main problem being the systematic communication from each vehicle to a unique, centralized station. In this work we tackle the problem by adopting the general view of distributed systems for the computation of a global function, consisting in minimizing the amount of information communicated through a careful coordination of the single nodes (vehicles) of the system. Our approach involves the use of predictive models that allow the central station to guess (in most cases and within some given error threshold) the location of the monitored vehicles and then to estimate the density of key areas without communications with the nodes.