Reliable Traffic Sensor Deployment Under Probabilistic Disruptions and Generalized Surveillance Effectiveness Measures

• Published in: Operations research Vol. 60; no. 5; pp. 1183 - 1198
• Main Authors: Li, Xiaopeng, Ouyang, Yanfeng
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.09.2012; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Approximation; Computer surveillance; CONTEXTUAL AREAS; continuum approximation; Heuristic; heuristics; Integer programming; Lagrange multiplier; Lagrangian relaxation; mixed-integer program; Motor vehicle traffic; reliability; Sensors; Studies; Surveillance; Technology application; Traffic control; Traffic engineering; Traffic estimation; traffic sensor deployment; Transportation; Transportation industry; Travel time; Vehicles; United States
• ISSN: 0030-364X; 1526-5463
• DOI: 10.1287/opre.1120.1082

Sensor systems as critical components of a transportation network provide a variety of real-time traffic surveillance information for traffic management and control. The deployment of sensors significantly affects their overall surveillance effectiveness. This paper proposes a reliable sensor location model to optimize surveillance effectiveness when sensors are subject to site-dependent probabilistic failures, and a general effectiveness measure is proposed to encompass most existing measures needed for engineering practice (e.g., flow volume coverage, vehicle-mile coverage, and squared error reduction). The problem is first formulated into a compact mixed-integer program, and we develop a variety of solution algorithms (including a custom-designed Lagrangian relaxation algorithm) and analyze their properties. We also propose alternative formulations including a continuum approximation model for single corridor problems and reliable fixed-charge sensor location models. Numerical case studies are conducted to test the performance of the proposed algorithms and draw managerial insights on how different parameter settings (e.g., failure probability and spatial heterogeneity) affect overall surveillance effectiveness and the optimal sensor deployment.