Traffic-Aware Navigation in Road Networks
This is an incremental comparison of existing algorithms for traffic-aware navigation in a specific road network, with no new methods introduced.
The project compared three graph search approaches for traffic-aware navigation in Kingston's road network, finding that Dijkstra's and A* provided the most traffic-aware optimal solutions with minimal preprocessing, while Floyd-Warshall-Ingerman was fastest but not traffic-aware, and Yen's algorithm balanced runtime and optimality with significant preprocessing.
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.