Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach

With the progress in intelligent transportation systems, a great interest has been directed towards traffic sensors information for flow estimation problems. Nevertheless, there is a great challenge to locate such traffic sensors on a network to attain the maximum benefits from them. Considering the O/D matrix estimation problem, all traffic sensors location models depend crucially on the reliability of the estimated matrix compared with a priori flow information. Thus, the required sensors number (cost) and locations for a network vary according to the estimation technique (e.g. least square, minimizing entropy, maximum likelihood, etc.) as well as the reliability of the priori information. Alternatively, this study presents a robust traffic sensor location model, which produces different trade-offs between the potential accuracy of the estimated O/D matrix and the cost of sensors' installation in a polynomial time complexity. The proposed approach searches for the number and locations of sensors that minimize the boundary of the maximum possible relative error for the estimated O/D matrix. The traffic sensor location problem is formulated as a set covering problem, then a multi-criteria meta-heuristics algorithm is adopted. The pioneer of this work is that it targets the maximum possible relative error directly in the multi-objective design process, which is considered a robust criterion for evaluating a solution set. Moreover, the proposed approach is extended to incorporate the screen line problem in a straightforward manner. For the purpose of validating the feasibility and the effectiveness of the proposed approach, two real networks are used. The results show the capability of producing the Pareto optimal (near optimal) solutions for any network.