Proactive Distributed Emergency Response with Heterogeneous Tasks Allocation

TL;DR

This paper introduces a proactive, distributed optimization framework for traffic incident management that considers incident interdependencies and incorporates UAVs to reduce incident delays effectively.

Contribution

It develops a novel DCOP-based proactive TIM model that accounts for incident evolution dependencies and integrates UAVs for enhanced situational awareness.

Findings

Significant reduction in total incident delay compared to traditional models

UAV support further decreases incident delay by 5% to 45%

Model demonstrates robustness across multiple TIM scenarios

Abstract

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring inherent interdependencies on the evolution of incidents in the environment while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences, and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops a new proactive framework based on the distributed constraint optimization problem (DCOP) to address the above limitations, overcoming conventional TIM models that cannot accommodate the dependencies in the TIM problem. Furthermore, the optimization objective is formulated to incorporate Unmanned Aerial Vehicles (UAVs). The UAVs' role in TIM includes exploring uncertain traffic conditions, detecting unexpected events, and augmenting information from roadway traffic sensors. Robustness analysis of our model for multiple TIM scenarios shows satisfactory performance using local search exploration heuristics. Overall, our model reports a significant reduction in total incident delay compared to conventional TIM models. With UAV support, we demonstrate a further decrease in the total incident delay ranging between 5% and 45% for the different number of incidents. UAV's active sensing can shorten response time of emergency vehicles, and a reduction in uncertainties associated with the estimated incident delay impact.