| Abstract |
The knowledge of the real time traffic flow status in each segment of a whole road network in a city or area is becoming fundamental for a large number of smart services such as: routing, planning, dynamic tuning services, healthy walk, etc. Rescue teams, police department, and ambulances need to know with high precision the status of the network in real time. On the other hand, the costs to obtain this information either with direct measures meant to add instruments on the whole network or acquiring data from international providers such as Google, TomTom, etc. is very high. The traditional modeling and computing approaches are not satisfactory since they are based on many assumptions that typically are doomed to change over time, as it occurs with traffic distribution at junctions; in short they cannot cover the whole network with the needed precision. In this paper, the above problem has been addressed providing a solution granting any traffic flow reconstruction with high precision and solving the indeterminacy of traffic distribution at junctions for large networks. The identified solution can be classified as a stochastic relaxation technique and resulted affordable on a parallel architecture based on GPU. The result has been obtained in the framework of the Sii-Mobility national project on smart city transport systems in Italy, a very large research project, and it is at present exploited in a number of cities/regions across Europe and by a number of research projects (Snap4City, TRAFAIR) of the European Commission. (C) 2020 The Authors. Published by Elsevier B.V. |