Continuous fastest path planning in road networks by mining real-timetraffic event information

Eric Hsueh Chan Lu, Chi Wei Huang, Vincent Shin-Mu Tseng

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In recent years, a number of studies had been done on the issues of fastestnavigation path planning due to wide applications. Most of previous studiesfocused on the fastest path planning by mining historical traffic logs. However,the real time traffic situations in the road network always vary continuouslydue to the occurrences of traffic events. Therefore, a better planning strategyshould take into account the effects of traffic events to avoid the trafficcongestions. In this paper, we propose a novel prediction-based method namedTraffic Event Prediction Algorithm (TEPA) for mining the traffic event knowledgewhich can be used to predict the effects of traffic events from historicaltraffic logs. In addition, we propose three continuous path planning strategiesfor finding the fastest path according to the real time traffic information.Finally, through a series of experiments, the proposed method is shown to haveexcellent performance under various system conditions. ICIC International

Original languageEnglish
Pages (from-to)969-974
Number of pages6
JournalICIC Express Letters
Issue number4
StatePublished - 1 Dec 2009


  • Data mining
  • Fastest path planning
  • Navigation system
  • Traffic event

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