Artificial Intelligence
Evacuation route planning: scalable heuristics
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Capacity constrained routing algorithms for evacuation planning: a summary of results
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
An evacuation planner algorithm in flat time graphs
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
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Evacuation planning is of critical importance for civil authorities to prepare for natural disasters, but efficient evacuation planning in large city is computationally challenging due to the large number of evacuees and the huge size of transportation networks. One recently proposed algorithm Capacity Constrained Route Planner (CCRP) can give sub-optimal solution with good accuracy in less time and use less memory compared to previous approaches. However, it still can not scale to large networks. In this paper, we analyze the overhead of CCRP and come to a new heuristic CCRP++ that scalable to large network. Our algorithm can reuse search results in previous iterations and avoid the repetitive global shortest path expansion in CCRP. We conducted extensive experiments with real world road networks and different evacuation parameter settings. The result shows it can gives great speed-up without loosing the optimality.