Optimization of network sensor location for full link flow observability considering sensor measurement error

Shao, M.; Xie, Congcong; Sun, Lijun

doi:10.1016/j.trc.2021.103460

Cited by 10 publications

(1 citation statement)

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“…Others presented case studies using sections of real road networks of moderate size (e.g. 300-500 road links) [17]. Two case studies on real-life networks focused on the NSLP for OD matrix estimation; a case study of the A3 Naples-Salerno motorway with 1,414 road links [9] and of the road network of the Province of Benevento in Italy with 1,800 road links [18].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

Sofokleous,

Englezou,

Savva

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under reallife conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.

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Section: Introductionmentioning

confidence: 99%