Data-driven robust location planning for UAV-assisted traffic incident management: Considering heterogeneous risk control

Abstract

With the rapid expansion of urban scale, traditional Ground-Based Response Units (GBRUs) face increasing challenges in maintaining efficient response times for traffic incidents, particularly under congestion and spatiotemporal uncertainty. This study develops an application-oriented strategic planning framework for an Unmanned Aerial Vehicle (UAV)-Assisted Traffic Incident Management System (UATIMS). The framework combines data-driven robust facility location with simulation-based operational validation. To represent the heterogeneous attributes of urban traffic incidents, we introduce an Urgency-Importance-Ambiguity (UIA) assessment framework that translates incident severity, network impact, and early-stage information uncertainty into planning-relevant risk categories. Based on this characterization, a weighted structural uncertainty set is constructed and embedded into a min-max robust location model to support capacity allocation under long-tail demand risks. The model incorporates a congestion-aware stability constraint and is reformulated into a tractable Mixed-Integer Linear Programming (MILP) model through strong duality. Using real-world traffic incident data from Hangzhou, China, we evaluate the proposed framework through computational experiments and Discrete Event Simulation (DES). The results show that the Full UIA model provides incremental value over homogeneous and urgency-weighted robust baselines, especially in reducing high-urgency service shortages and tail response risks under resource-constrained conditions. The simulation further indicates that the resulting UAV-assisted layout can achieve high net service coverage and maintain average response times within an operationally acceptable range. These findings suggest that heterogeneous risk representation can complement existing drone-service design models by supporting strategic hangar siting and fleet allocation for traffic incident management.

Graphical abstract

Keywords

UAV-assisted traffic management system / emergency response / robust optimization on facility location / heterogeneous risk control / mixed-integer linear programming

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