徐秀娟

个人信息Personal Information

副教授

硕士生导师

性别:女

毕业院校:吉林大学

学位:博士

所在单位:软件学院、国际信息与软件学院

学科:软件工程

办公地点:开发区综合楼

电子邮箱:xjxu@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

A Novel Algorithm for Urban Traffic Congestion Detection Based on GPS Data Compression

点击次数:

论文类型:会议论文

发表时间:2016-07-10

收录刊物:EI、CPCI-S、Scopus

页面范围:107-112

关键字:Traffic Congestion; Intelligent Transportation System; Data Compression; Trajectory

摘要:Traffic congestion exists in every big city of China. This paper designs a novel traffic congestion detection algorithm from two aspects. One is the offline traffic data processing and the other is congestion mode judgment by online monitoring. The offline data processing includes two pars: spatial information and temporal information in the trajectories. A trajectory is represented by a spatial path and a temporal sequence. This representation supports different compression approaches for spatial information and temporal information respectively, so that both spatial compression and temporal compression can achieve high compression effectiveness. The online monitoring is as following. Traffic congestion model is based on three parameters of traffic jams (average speed, density, traffic flow), then configured parameter values were calculated based on traffic data. Base on the rule of congestion threshold by city traffic management evaluation system, urban road design requirements and highways service level analysis of indicators and grading standards, we use standard function method to calculate the parameters of standardized integrated transport threshold, and then quantify the impact of each characteristic parameter congestion to achieve the goal. Finally, the road congestion is determined, and implement the traffic congestion judgment visualization.