Abstract
Traffic camera has become a popular sensor for traffic state estimation in Intelligent Transportation Systems (ITS). However, it is impracticable to cover the whole urban network. Then how to place sensors in a certain network to ensure the accuracy for both installed and uninstalled roads is of great importance. The GPS-equipped taxis traveling on the urban roads yield big GPS data, which contains mass traffic information. In this paper, a traffic state network (TSN) will be presented to model the relationship among all road sections based on these data. In TSN, the location problem is transformed to find a set of nodes in the network to maximize the received information. Then, it is verified NP-hard, and a local optimal solution is presented to solve it in a linear complexity time based on greedy algorithm. The experiments were carried out on the GPS data from 8007 taxis traveling on 1432 sections for 197 days. The results show that the TSN effectively describes the relationship of road sections in traffic state, and performs better than random road location method and arterial road location method.
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