| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 95 === The occurrence of traffic congestion has been increasing around world-wide as the result of the increasing of motorization, urbanization, population growth and changes in population density, especially in Urban Network; therefore, many researches are proposed to improve the traffic congestion; moreover, finding the traffic bottlenecks is the most important thing to improve the traffic congestion. As we know, freeway bottlenecks are always fixed and well known as gateway but the urban network bottlenecks may vary with spatial and temporal environment; therefore, finding out urban network bottleneck becomes a very difficult but very important mission. We propose a Spatiotemporal Traffic Bottleneck Mining Model (STBM) in this thesis to discover the urban network bottlenecks based on three heuristics we developed. In this thesis, STBM prototype model is implemented based on a real time LBS-based application to find out the Taipei urban network bottlenecks. Experimental results show that the average accuracy in workday of STBM is up to 80% and it‟s better than the traditional statistic model. In the near future, the STBM model could be implemented as a real time bottleneck detection and prediction system, which integrates the historical traffic patterns and real-time traffic information.
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