article · arXiv (Cornell University) · 2026
Traffic-Aware Navigation in Road Networks
Cited by 0Open access
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.
Cite this paper
Nassar, S. (2026). Traffic-Aware Navigation in Road Networks. arXiv (Cornell University).
Nassar, Sarah. “Traffic-Aware Navigation in Road Networks.” arXiv (Cornell University), 2026.
Nassar, Sarah. 2026. “Traffic-Aware Navigation in Road Networks.” arXiv (Cornell University).
Nassar, S. (2026) ‘Traffic-Aware Navigation in Road Networks’, arXiv (Cornell University).
@article{nassar2026traffic,
title = {{Traffic-Aware Navigation in Road Networks}},
author = {Nassar, Sarah},
journal = {arXiv (Cornell University)},
year = {2026}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.