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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/224294
- Title
- Traffic aware route planning in dynamic road networks
- Author(s)
- Xu, Jiajie; Guo, Limin; Ding, Zhiming; Sun, Xiling; Liu, Chengfei
- Abstract
- 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.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies
- Source
- Lecture Notes in Computer Science: proceedings of the 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012), Busan, South Korea, 15-18 April 2012 / Sang-goo Lee, Zhiyong Peng, Xiaofang Zhou, Yang-Sae Moon, Rainer Unland and Jaesoo Yoo (eds.), Vol. 7238, pp. 576-591
- Publication year
- 2012
- Keyword(s)
- Road networks; Route planning; Spatial-temporal knowledge; Traffic aware route planning; Traffic conditions; Traffic networks
- Publisher
- Springer
- ISSN
- 0302-9743 (series ISSN)
- ISBN
- 9783642290374, 364229037X
- Publisher URL
- http://dx.doi.org/10.1007/978-3-642-29038-1_41
- Copyright
- Copyright © Springer-Verlag Berlin Heidelberg 2012.
- Research Projects
-
On effectively modelling and efficiently discovering communities from large networks, Australian Research Council grant number DP120102627
- Peer reviewed
