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Traffic-Aware Navigation in Road Networks

Sarah Nassar

TL;DR

The paper addresses traffic-aware routing in urban road networks by comparing three graph-search strategies on Kingston's road network: Floyd-Warshall-Ingerman (multi-query, distance-only costs), single-query Dijkstra's/A* (with traffic-aware edge costs and heuristics), and Yen's K-shortest paths (K=$5$). Dijkstra's and A* deliver traffic-optimal routes with minimal preprocessing, Floyd-Warshall-Ingerman yields the fastest real-time results but ignores traffic effects, and Yen's provides a middle ground at substantial preprocessing cost. The study also examines the impact of incorporating speed limits into edge costs, demonstrating robustness of the main trends with nuanced changes in heuristic admissibility. These findings guide deployment decisions for GPS and robotics planners under dynamic traffic conditions and highlight areas for improving preprocessing efficiency and edge-cost modeling.

Abstract

This project compares three graph search approaches for the task of traffic-aware navigation in Kingston's road network. These approaches include a single-run multi-query preprocessing algorithm (Floyd-Warshall-Ingerman), continuous single-query real-time search (Dijkstra's and A*), and an algorithm combining both approaches to balance between their trade-offs by first finding the top K shortest paths then iterating over them in real time (Yen's). Dijkstra's and A* resulted in the most traffic-aware optimal solutions with minimal preprocessing required. Floyd-Warshall-Ingerman was the fastest in real time but provided distance based paths with no traffic awareness. Yen's algorithm required significant preprocessing but balanced between the other two approaches in terms of runtime speed and optimality. Each approach presents advantages and disadvantages that need to be weighed depending on the circumstances of specific deployment contexts to select the best custom solution. *This project was completed as part of ELEC 844 (Search and Planning Algorithms for Robotics) in the Fall 2025 term.

Traffic-Aware Navigation in Road Networks

TL;DR

The paper addresses traffic-aware routing in urban road networks by comparing three graph-search strategies on Kingston's road network: Floyd-Warshall-Ingerman (multi-query, distance-only costs), single-query Dijkstra's/A* (with traffic-aware edge costs and heuristics), and Yen's K-shortest paths (K=). Dijkstra's and A* deliver traffic-optimal routes with minimal preprocessing, Floyd-Warshall-Ingerman yields the fastest real-time results but ignores traffic effects, and Yen's provides a middle ground at substantial preprocessing cost. The study also examines the impact of incorporating speed limits into edge costs, demonstrating robustness of the main trends with nuanced changes in heuristic admissibility. These findings guide deployment decisions for GPS and robotics planners under dynamic traffic conditions and highlight areas for improving preprocessing efficiency and edge-cost modeling.

Abstract

This project compares three graph search approaches for the task of traffic-aware navigation in Kingston's road network. These approaches include a single-run multi-query preprocessing algorithm (Floyd-Warshall-Ingerman), continuous single-query real-time search (Dijkstra's and A*), and an algorithm combining both approaches to balance between their trade-offs by first finding the top K shortest paths then iterating over them in real time (Yen's). Dijkstra's and A* resulted in the most traffic-aware optimal solutions with minimal preprocessing required. Floyd-Warshall-Ingerman was the fastest in real time but provided distance based paths with no traffic awareness. Yen's algorithm required significant preprocessing but balanced between the other two approaches in terms of runtime speed and optimality. Each approach presents advantages and disadvantages that need to be weighed depending on the circumstances of specific deployment contexts to select the best custom solution. *This project was completed as part of ELEC 844 (Search and Planning Algorithms for Robotics) in the Fall 2025 term.
Paper Structure (15 sections, 7 figures, 4 tables)

This paper contains 15 sections, 7 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODS
  4. Data Preparation
  5. Algorithms
  6. Metrics
  7. Experimental Setup
  8. RESULTS
  9. Traffic-Weighted Distance as Cost
  10. Statistical Analysis
  11. Incorporating Speed Limit into Edge Cost
  12. DISCUSSION
  13. Hypotheses Revisited
  14. Future Work
  15. CONCLUSION

Figures (7)

  • Figure 1: Graph representation of Kingston's road network, downloaded and plotted with the OSMnx Python package.
  • Figure 2: Subset of rows and columns from edges DataFrame.
  • Figure 3: Probability distribution of low (green), medium (orange), and high (red) traffic weights for each set of trials.
  • Figure 4: Histogram of Euclidean distance between each of the 1,000 start-goal intersection pairs.
  • Figure 5: Average cost breakdown by trial traffic conditions with traffic-weighted distance as edge cost, ranging from no traffic to heavy traffic.
  • ...and 2 more figures