Adaptive Optimization of Traffic Sensor Locations Under Uncertainty Using Flow-Constrained Inference

Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor...

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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 18; p. 10257
Main Authors: Owais, Mahmoud, Allam, Amira A.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2025
Subjects:
Algorithms
binary bat algorithm
combinatorial optimization
integer programming
LU factorization
Measuring instruments
metaheuristics
Optimization
Propagation
Sensors
Traffic flow
traffic sensor location
United States
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor placements needed to achieve full link flow observability. Existing solutions primarily rely on algebraic or optimization-based approaches, but often neglect the impact of sensor measurement errors and struggle with scalability in large, complex networks. This study proposes a new scalable and robust methodology for solving the TSLP under uncertainty, incorporating a formulation that explicitly models the propagation of measurement errors in sensor data. Two nonlinear integer optimization models, Min-Max and Min-Sum, are developed to minimize the inference error across the network. To solve these models efficiently, we introduce the BBA Algorithm (BBA) as an adaptive metaheuristic optimizer, not as a subject of comparative study, but as an enabler of scalability within the proposed framework. The methodology integrates LU decomposition for efficient matrix inversion and employs a node-based flow inference technique that ensures observability without requiring full path enumeration. Tested on benchmark and real-world networks (e.g., fishbone, Sioux Falls, Barcelona), the proposed framework demonstrates strong performance in minimizing error and maintaining scalability, highlighting its practical applicability for resilient traffic monitoring system design.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app151810257