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Proactive Distributed Emergency Response with Heterogeneous Tasks Allocation

Justice Darko, Hyoshin Park

TL;DR

This paper tackles the inefficiency of traditional traffic incident management (TIM) by introducing a proactive, DCOP-based framework (P-DRONETIM) that coordinates ground Emergency Response Vehicles (ERVs) and Unmanned Aerial Vehicles (UAVs) with a look-ahead horizon. The approach models resource allocation as a Proactive Dynamic Routing of unmanned-aerial and Emergency Team Incident Management (P-DRONETIM) problem, incorporating stochastic incident occurrences, two-stage anticipation, and data assimilation to refine delay estimates. Ground and aerial subteams are coupled through complete DCOP constraints and a UAV deployment priority matrix, with solution via local-search algorithms MGM and DSA, including an explicit look-ahead of h=2. Experiments on a 10x10 grid show that P-DRONETIM reduces total network delay by meaningful margins, and UAV cooperation further lowers delays by 5–45% while also reducing uncertainty in delay estimates, highlighting the practical potential of drones as first responders in TIM.

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.

Proactive Distributed Emergency Response with Heterogeneous Tasks Allocation

TL;DR

This paper tackles the inefficiency of traditional traffic incident management (TIM) by introducing a proactive, DCOP-based framework (P-DRONETIM) that coordinates ground Emergency Response Vehicles (ERVs) and Unmanned Aerial Vehicles (UAVs) with a look-ahead horizon. The approach models resource allocation as a Proactive Dynamic Routing of unmanned-aerial and Emergency Team Incident Management (P-DRONETIM) problem, incorporating stochastic incident occurrences, two-stage anticipation, and data assimilation to refine delay estimates. Ground and aerial subteams are coupled through complete DCOP constraints and a UAV deployment priority matrix, with solution via local-search algorithms MGM and DSA, including an explicit look-ahead of h=2. Experiments on a 10x10 grid show that P-DRONETIM reduces total network delay by meaningful margins, and UAV cooperation further lowers delays by 5–45% while also reducing uncertainty in delay estimates, highlighting the practical potential of drones as first responders in TIM.

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.
Paper Structure (25 sections, 15 equations, 13 figures, 3 tables)

This paper contains 25 sections, 15 equations, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Overview of Optimization Technique
  4. Simulation-Based Framework deqi2012simulation:
  5. Bi-level Algorithms duan2015emergencyYinwang2020research:
  6. Scenario-Based Algorithms Yin1:
  7. Work Function Algorithms with Look-aheadPark1:
  8. State-of-the-art Traffic Incident Management
  9. Emergency Vehicle Team (EVT) in TIM
  10. Proactive-DRONETIM Model
  11. Approach overview
  12. Proactive Dynamic EVT in TIM as DCOP
  13. Probability of incident occurrences.
  14. Total delay impact on network due to incident.
  15. TIM with UAV Active Sensing
  16. ...and 10 more sections

Figures (13)

  • Figure 1: Emergency vehicle team in TIM with three agents and two incidents. Grid cells represent the possible locations an emergency vehicle can visit, and stars represent the incidents, characterized by their expected total delay on the network for each time step.
  • Figure 2: Dependency in incident occurrences.P2
  • Figure 3: TIM with UAV active sensing.
  • Figure 4: UAV deployment priority matrix based on hazard index of the ERVs route-to-incident location $l$, delay impact uncertainty (sparsity of sensor network at incident location), and incident type (severity).
  • Figure 5: MGM and DSA (varying probability threshold) solution and convergence for total delay impacting network (Incident requests = 5, ERVs = 3.)
  • ...and 8 more figures