Introduction to algorithms
IEEE Transactions on Knowledge and Data Engineering
Materialization Trade-Offs in Hierarchical Shortest Path Algorithms
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Materialization and Incremental Update of Path Information
Proceedings of the Fifth International Conference on Data Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Artificial Intelligence
Roads, codes, and spatiotemporal queries
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient query processing on spatial networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Adaptive nearest neighbor queries in travel time networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Finding Fastest Paths on A Road Network with Speed Patterns
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Monitoring minimum cost paths on road networks
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The one-shot shortest path query has been studied for decades. However, in the applications on road networks, users are actually interested in the path with the minimum travel time (the fastest path), which varies as time goes. This motivates us to study the continuous evaluation of fastest path queries in order to capture the dynamics of road networks. Repeatedly evaluating a large number of fastest path queries at every moment is infeasible due to its computationally expensive cost. We propose a novel approach that employs the concept of the affecting area and the tolerance parameter to avoid the reevaluation while the travel time of the current answer is close enough to that of the fastest path. Furthermore, a grid-based index is designed to achieve the efficient processing of multiple queries. Experiments on real datasets show significant reduction on the total amount of reevaluation and therefore the cost for reevaluating a query.