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A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring

Yumeng Bai, Yiheng Feng

TL;DR

This work tackles long-term urban traffic monitoring with UAVs by introducing a dynamic routing framework that leverages ground vehicles as mobile charging stations. Each multi-UAV, single-flight is modeled as a TAOP-DP to maximize spatiotemporal network coverage, with a novel information loss curve guiding weight updates between flights to reflect evolving traffic and revisit intervals. A multi-flight heuristic decomposes the overall task into sequential single-flights, updating edge priorities using observed data and the information loss curve, validated through microscopic simulations on a modified Sioux Falls network and MFD reconstruction. Results show robust performance, especially when historical data are incomplete, and demonstrate the framework’s capability to capture network-wide traffic trends, highlighting practical potential for network-level traffic control and management.

Abstract

Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring due to their rapid speed, cost-effectiveness, and extensive field-of-view, while being unconstrained by traffic congestion. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the evolving nature of urban traffic networks. In this study, we introduce a novel dynamic UAV routing framework for long-term, network-wide urban traffic monitoring, leveraging existing ground vehicles as mobile charging stations without disrupting their operations. To address the complexity of long-term monitoring scenarios involving multiple flights, we decompose the problem into manageable single-flight tasks, in which each flight is modeled as a Team Arc Orienteering Problem with Decreasing Profits with the objective to collectively maximize the spatiotemporal network coverage. Between flights, we adaptively update the edge weights to incorporate real-time traffic changes and revisit intervals. We validate our framework through extensive microscopic simulations in a modified Sioux Falls network under various scenarios. Comparative results demonstrate that our model outperforms three baseline approaches, especially when historical information is incomplete or absent. Moreover, we show that our monitoring framework can capture network-wide traffic trends and construct accurate Macroscopic Fundamental Diagrams (MFDs). These findings demonstrate the effectiveness of the proposed dynamic UAV routing framework, underscoring its suitability for efficient and reliable long-term traffic monitoring. Our approach's adaptability and high accuracy in capturing the MFD highlight its potential in network-wide traffic control and management applications.

A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring

TL;DR

This work tackles long-term urban traffic monitoring with UAVs by introducing a dynamic routing framework that leverages ground vehicles as mobile charging stations. Each multi-UAV, single-flight is modeled as a TAOP-DP to maximize spatiotemporal network coverage, with a novel information loss curve guiding weight updates between flights to reflect evolving traffic and revisit intervals. A multi-flight heuristic decomposes the overall task into sequential single-flights, updating edge priorities using observed data and the information loss curve, validated through microscopic simulations on a modified Sioux Falls network and MFD reconstruction. Results show robust performance, especially when historical data are incomplete, and demonstrate the framework’s capability to capture network-wide traffic trends, highlighting practical potential for network-level traffic control and management.

Abstract

Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring due to their rapid speed, cost-effectiveness, and extensive field-of-view, while being unconstrained by traffic congestion. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the evolving nature of urban traffic networks. In this study, we introduce a novel dynamic UAV routing framework for long-term, network-wide urban traffic monitoring, leveraging existing ground vehicles as mobile charging stations without disrupting their operations. To address the complexity of long-term monitoring scenarios involving multiple flights, we decompose the problem into manageable single-flight tasks, in which each flight is modeled as a Team Arc Orienteering Problem with Decreasing Profits with the objective to collectively maximize the spatiotemporal network coverage. Between flights, we adaptively update the edge weights to incorporate real-time traffic changes and revisit intervals. We validate our framework through extensive microscopic simulations in a modified Sioux Falls network under various scenarios. Comparative results demonstrate that our model outperforms three baseline approaches, especially when historical information is incomplete or absent. Moreover, we show that our monitoring framework can capture network-wide traffic trends and construct accurate Macroscopic Fundamental Diagrams (MFDs). These findings demonstrate the effectiveness of the proposed dynamic UAV routing framework, underscoring its suitability for efficient and reliable long-term traffic monitoring. Our approach's adaptability and high accuracy in capturing the MFD highlight its potential in network-wide traffic control and management applications.
Paper Structure (33 sections, 5 equations, 15 figures, 3 tables, 2 algorithms)

This paper contains 33 sections, 5 equations, 15 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. FRAMEWORK OVERVIEW
  3. METHODOLOGY
  4. Single-flight UAV Routing Problem Formulation
  5. Dynamic UAV Routing Framework for Multiple Flights
  6. Information Loss Curve
  7. Multi-Flight Heuristic Algorithm.
  8. CASE STUDY
  9. Simulation Environment Setup
  10. Information Loss Curve Generation
  11. Experimental Design
  12. Baseline Models for Comparison
  13. Baseline with Linear Information Loss Curve (Linear Info)
  14. Baseline without Information Loss Curve (No Info)
  15. OTOP-RV Baseline
  16. ...and 18 more sections

Figures (15)

  • Figure 1: Conceptual overview of the proposed UAV-Based traffic monitoring framework.
  • Figure 2: Overview of the dynamic UAV routing framework.
  • Figure 3: Bidirectional Sioux Falls network with edge demand levels.
  • Figure 4: Information loss curve calibration result.
  • Figure 5: Comparison of information loss curves.
  • ...and 10 more figures