|Table of Contents|

Regional traffic state evaluation method based on data visualization(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年01期
Page:
133-140
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Regional traffic state evaluation method based on data visualization
Author(s):
HE Zhao-cheng;  ZHOU Ya-qiang;  YU Zhi
Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou 510275, Guangdong, China
Keywords:
traffic engineering;  traffic state analysis;  road network operation evaluation;  data visualization;  congestion pattern;  spatial clustering
PACS:
U491.11
DOI:
-
Abstract:
The spatio-temporal accumulation index of traffic congestion was introduced to identify and quantitatively analyze regional traffic operation state. The functional relationship between congestion source and congestion evaluation point was built to construct visual model. The histogram of oriented gradient and the main component analysis method were used to carry out the characteristics extraction of traffic operation state data. Gaussian mixture clustering method was used to cluster characteristic data and devide the spatial distribution model of regional traffic congestion. 23 478 taxis in Guangzhou were chosen and 509 376 data samples were obtained. The congestion patterns of traffic road network were recognized and partitioned, and the distribution characteristics of urban traffic congestion were analyzed and evaluated. Test result shows that the average congestion intensities for three traffic samples are 0.558, 0.559 and 0.559 respectively, the traffic congestion aggregation indexes are 3.518, 3.121 and 2.800 respectively, so total road network congestion intensities for three samples are same, but there are big differences on spatial congestion distributions. When the congestion level is 6 in Guangzhou, the numbers of main aggregation regions for three congestion models are 2, 4 and 3 respectively, which accords with the practical survey result. The method proposed in this paper can depict the degree and distribution of regional traffic congestion, and describe the urban regional traffic operational patterns in spatial dimension more scientifically and directly. 2 tabs, 15 figs, 19 refs.

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Last Update: 2016-02-20