A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors

Abstract

Monitoring vehicles’ paths is important for the management and governance of smart sustainable cities, where traffic sensors play a significant role. As a typical sensor, an automatic vehicle identification (AVI) sensor can observe the whereabouts and movements of vehicles. In this article, we introduced an indicator called the deployment score to present the deployment priorities of AVIs for a better reconstruction of vehicles’ paths. The deployment score was obtained based on a programming method for maximizing the accuracy of a recurring vehicle’s path and minimizing the number of AVI sensors. The calculation process is data-driven, where a random-work method was developed to simulate massive path data (tracks of vehicles) according to travel characteristics extracted from finite GPS data. Then, for each simulated path, a path-level bi-level programming model (P-BPM) was constructed to find the optimal layout of the AVI sensors. The solutions of the P-BPM proved to be approximate Pareto optima from a data-driven perspective. Furthermore, the PageRank method was presented to integrate the solutions; thus, the deployment score was obtained. The proposed method was validated in Chengdu City, whose results demonstrated the remarkable value of our approach.

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

“…A case in Shanghai showed that the proposed method was superior to other algorithms. Li et al [12] introduced a deployment score indicator to present the deployment priorities of AVI sensors for a better reconstruction of vehicles' paths. Then, a random work method was developed to simulate massive path data, and a path-level bi-level programming model (P-BPM) was constructed to find the optimal layout of the AVI sensors for each simulated path.…”