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

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Abstract

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.

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Data availability

Data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors acknowledge the Tehran Urban Research and Planning Center for moral and financial support.

Funding

A grant was provided to the second author, F. Vahdat, by the Tehran Urban Research and Planning Center (Grant Number: 137/843697).

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    F. Hooshmand, F. Vahdat & S. A. MirHassani

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  1. F. Hooshmand
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  2. F. Vahdat
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  3. S. A. MirHassani
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Contributions

All authors contributed to the study conception, modeling, methodology, data collection, analysis, and writing. They all read and approved the paper.

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Correspondence to F. Hooshmand.

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Hooshmand, F., Vahdat, F. & MirHassani, S.A. A sensor location model and an efficient GA for the traffic volume estimation. Soft Comput 28, 2987–3013 (2024). https://doi.org/10.1007/s00500-023-09228-z

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