Fully dynamic algorithms for maintaining shortest paths trees
Journal of Algorithms
Shortest Path Algorithms: An Evaluation Using Real Road Networks
Transportation Science
Artificial Intelligence
Two ellipse-based pruning methods for group nearest neighbor queries
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Heuristic shortest path algorithms for transportation applications: state of the art
Computers and Operations Research
A new algorithm for reoptimizing shortest paths when the arc costs change
Operations Research Letters
A hybrid evolutionary-graph approach for finding functional network paths
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
An agent-based model for simulation of traffic network status
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
| Hi-index | 0.00 |
Finding an optimal route in dynamic real-time transportation networks is a critical problem for vehicle navigation. Existing approaches are either too complex or incapable of managing complex circumstances where both the location of a mobile object and traffic conditions change over time. In this paper, we propose an incremental search approach with novel heuristics based on a variation of the A* algorithm-Lifelong Planning A*. In addition, we suggest using an ellipse to prune the unnecessary nodes to be scanned in order to speed up the dynamic search process. The proposed algorithm determines the shortest-cost path between a moving object and its destination by continually adapting to the dynamic traffic conditions, while making use of the previous search results. Experimental results evince that the proposed algorithm performs significantly better than the well-known A* algorithm.