Artificial Intelligence
Finding Fastest Paths on A Road Network with Speed Patterns
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Finding time-dependent shortest paths over large graphs
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Efficiently indexing shortest paths by exploiting symmetry in graphs
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic path queries in road networks: traffic uncertainty aware path selection
Proceedings of the 13th International Conference on Extending Database Technology
Fast top-k simple shortest paths discovery in graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Graph indexing of road networks for shortest path queries with label restrictions
Proceedings of the VLDB Endowment
A continuous query system for dynamic route planning
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
A critical-time-point approach to all-start-time lagrangian shortest paths: a summary of results
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
On trip planning queries in spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Effective map-matching on the most simplified road network
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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The current widespread use of GPS navigations and trip planning on web has aroused great interests in fast and scalable path query processing. Recent research has mainly focused on static route optimisation where the traffic network is assumed to be stable. However in most cases, route planning is in presence of frequent updates to the traffic graph due to the dynamic nature of traffic network, and such updates always greatly affect the performance of route planning. Most existing methods, however, cannot effectively support traffic aware route planning. In this paper, a new strategy is proposed to handle this problem. We analysis the traffic condition on the road network and explore spatial-temporal knowledge to guide effective route planning. In particular, a set of effective techniques are used to avoid both unnecessary calculations on huge graph and excessive re-calculations caused by traffic condition updates. A comprehensive experiment is also conducted to evaluate the strategy performances.