基于轨迹压缩的城市交通拥堵识别与跟踪研究

项目名称： 基于轨迹压缩的城市交通拥堵识别与跟踪研究

项目编号： No.61502069

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 徐秀娟

作者单位： 大连理工大学

项目金额： 21万元

中文摘要： 路网数据、兴趣点数据和浮动车轨迹数据是城市交通的数字足迹，由于这些移动数据与时间和位置高度相关，使得传统的数据挖掘方法难以在实际中得到广泛应用。.针对轨迹数据的城市交通拥堵识别与跟踪问题，历史数据包含丰富的信息不应简单丢弃，而已有的算法很少同时考虑历史数据和实时数据。本课题提出了基于离线轨迹压缩和在线增量更新的拥堵演化分析。拟深入研究1）基于路网分割，利用最短路径和频繁轨迹的离线轨迹压缩模型，并设置横纵索引方便快速检索和更新数据；2）基于轨迹压缩模型的在线轨迹增量更新方法；3）基于速度和密度计算道路的通行效率，利用通行效率识别拥堵，通过轨迹压缩结构之间的轨迹快照差异跟踪拥堵；同时分析与道路结构相关的拥堵成因问题。通过本课题的研究为探索拥堵演化规律提供科学依据，对阐明轨迹数据的机制有着重要意义。针对移动数据的要求所开发的高效在线增量更新算法，也可以推广到其他复杂大数据的模式识别和跟踪问题中。

中文关键词： 动态数据挖掘；挖掘模型；挖掘算法；增量挖掘

英文摘要： The data about road network, the point of interest and floating car track data are digital footprint of city traffic. Because of these mobile data are related to time and location, frequent pattern discovery method from the traditional data mining could not be widely used in practice..The existing algorithms are seldom considered the historical data and real-time data together, however historical data includes plentiful information which could not be simply discarded. Aiming at the congestion identification and tracking problem of tracking data, this project will study deeply: 1) proposed an offline trajectory compression model based on network segmentation by the shortest path and frequent trajectories, and set the vertical and horizontal index to facilitate fast retrieval and update data; and update trajectory compression model incrementally; 2) proposed an online trajectory incrementally updating model based on trajectory compression model. 3）computed the road traffic efficiency based on velocity and density, identified congestion by traffic efficiency, tracked the status of congestion based on the differences of two nearly compression snapshots; meanwhile, analyze congestion causes problems related to road structure..We study the models of identification and tracking traffic congestion based on trajectory data, provide scientific basis for exploring the evolution of congestion. It is important to elucidate the mechanism based on trajectory data. The developed algorithms could be not only effectively online incrementally updated for retrieval, modeling and updating of mobile data, but also can be extended to the problem of pattern recognition and tracking in other large complex data.

英文关键词： dynamic data mining;mining model;mining algorithm;incremental model