Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Fakhouri, Ashkan Ahmadi; Soltani, Roya

doi:10.1109/access.2020.3047045

Cited by 3 publications

(1 citation statement)

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“…Lessin, Lunday and Hill (2018) develop a bilevel optimization model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder's penetration path through a defended region. Fakhouri and Soltani (2020) propose a new multi-objective model to find the optimal location of wireless sensors along highways to control traffic flow and monitor highways. Vieria, Ferrari, Ribeiro, Bahiense, Orrico Filho, Abramides and Júnior (2020) propose a progressive hybrid algorithm based on exact, heuristic and hybrid approaches embedded on a set covering framework to solve the traffic counting location problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Plate Recognition Sensor Locations: A Case Study in Turkey

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2021

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With the advances in sensor and data transfer technologies, the usage areas of Automatic License Plate Recognition (ALPR) systems have been expanded in the public and private sectors. In public safety, ALPR systems are used to monitor and control traffic data at both individual and collective levels. To build an efficient sensor network, the locations of ALPR systems should be determined optimally. This study provides an approach to determine optimal locations of ALPR systems that maximize network coverage consisting of two measures: i) vehicle coverage and ii) road coverage. The former represents the daily average vehicle flow whereas the latter stands for the number of road-links covered. The relative importance of vehicle and road coverages are taken into consideration, and optimal solutions under various scenarios are presented. A close neighbor constraint is introduced to avoid inefficient distribution of ALPR systems on the network. A case study with numerical examples designed for two cities in Turkey is provided. The centralized and decentralized solutions are compared against the current state, and the results show that the network coverage increases substantially in the centralized case.

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Section: Literature Reviewmentioning

confidence: 99%