A sensor location model and an efficient GA for the traffic volume estimation

This paper addresses the problem of locating vehicle-identification sensors along the arcs of the transportation network. The aim is to estimate the traffic volumes for a given set of routes under the assumption that the available sensors are insufficient to uniquely identify all route flows. We pre...

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Bibliographic Details
Published in:	Soft computing (Berlin, Germany) Vol. 28; no. 4; pp. 2987 - 3013
Main Authors:	Hooshmand, F., Vahdat, F., MirHassani, S. A.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2024 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computational Intelligence Control Engineering Genetic algorithms Integer programming License plates Linear equations Linear programming Mathematical Logic and Foundations Mathematical models Mechatronics Mixed integer Optimization Robotics Sensors Traffic volume Transportation networks Vehicle identification Vehicles Partial estimation of traffic volumes Cluster Genetic algorithm Sensor location problem Success ratio of operators
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Summary:	This paper addresses the problem of locating vehicle-identification sensors along the arcs of the transportation network. The aim is to estimate the traffic volumes for a given set of routes under the assumption that the available sensors are insufficient to uniquely identify all route flows. We present a novel mixed-integer linear programming (MILP) model to determine the sensor locations so that in the system of linear equations solved in the path reconstruction phase, those routes whose volume cannot be uniquely determined, are linked to each other by equations involving a small number of unknowns. By this approach, experts’ opinions or historical information can be used to give a more precise estimation for those routes whose volumes are not uniquely observable. Since the direct resolution of the model via MILP solvers is time-consuming over moderate- and large-sized instances, by utilizing the problem structure, a genetic algorithm is adopted to find high-quality solutions to the model. Computational experiments over different instances, taken from the literature, confirm the effectiveness of the proposed model and algorithm.
ISSN:	1432-7643 1433-7479
DOI:	10.1007/s00500-023-09228-z