| Abstract |
Due to growing cities, the real-time knowledge about the state of traffic is a critical problem for urban mobility and the road-segments traffic densities estimation influences the efficiency of fundamental smart city services as smart routing, smart planning for evacuations, planning of civil works on the city, etc. Nevertheless, the traffic-related data from navigator Apps (e.g., TomTom, Google, Bing) are too expensive to be acquired. Also, the traditional sensors for the traffic flow detection are very expensive, and they are usually not dense enough for a correct traffic monitoring. In order to overcome such problems, there is a space for low cost and fast solutions for dense traffic flow reconstruction. We propose a real-time visual self-adaptive solution to reconstruct the traffic density at every location of a wide urban area leveraging the detections from a few fixed traffic sensors deployed within the area of interest. A such method is based on fluid dynamic models to simulate macroscopic phenomena as shocks formation and propagation of waves backwards along roads. Such physical constraints are applied to a detailed street graph which is enriched by specific parameters representing a weight in terms of traffic road capacity. The weight's assignment has been estimated by a stochastic learning approach at each time slot of the day. The accuracy of the proposed model comes from the error between the reconstructed traffic density and the measured values at the sensor position by excluding each sensor iteratively and reconstructing the flow without it. The proposed reconstruction model has been created by exploiting open and real-time data in the context of Sii-Mobility research project by using Km4City infrastructure in the area of Florence, Italy, for its corresponding Smart City solution. |
