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Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories
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作者 Tao XU Xiang LI Christophe CLARAMUNT 《Frontiers of Earth Science》 SCIE CAS CSCD 2018年第2期253-263,共11页
Accurate travel time prediction is undoubtedlyof importance to both traffic managers and travelers. Inhighly-urbanized areas, trip-oriented travel time prediction(TOTTP) is valuable to travelers rather than trafficm... Accurate travel time prediction is undoubtedlyof importance to both traffic managers and travelers. Inhighly-urbanized areas, trip-oriented travel time prediction(TOTTP) is valuable to travelers rather than trafficmanagers as the former usually expect to know the traveltime of a trip which may cross over multiple road sections.There are two obstacles to the development of TOTTP,including traffic complexity and traffic data coverage. Withlarge scale historical vehicle trajectory data and meteorol-ogy data, this research develops a BPNN-based approachthrough integrating multiple factors affecting trip traveltime into a BPNN model to predict trip-oriented travel timefor OD pairs in urban network. Results of experimentsdemonstrate that it helps discover the dominate trends oftravel time changes daily and weekly, and the impact ofweather conditions is non-trivial. 展开更多
关键词 trip-oriented travel time prediction (TOTTP) urban network Back Propagation Neural Networks(BPNN) historical vehicle trajectories
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