Fine-grained analysis of traffic congestions at the turning level using GPS traces
For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis&...
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doaj-745b605d407f4b47bd6330d713ebcc012020-11-24T21:50:28ZzhoSurveying and Mapping PressActa Geodaetica et Cartographica Sinica1001-15951001-15952018-01-01481758510.11947/j.AGCS.2019.201704482019010448Fine-grained analysis of traffic congestions at the turning level using GPS tracesTANG Luliang0KAN Zihan1REN Chang2ZHANG Xia3LI Qingquan4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Urban Design, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaFor the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis' operating patterns and filtered valid traces. Then this approach detected traffic congestion traces of three different intensities:mild congestion, moderate congestion and serious congestion, based on analyzing traffic conditions from the filtered valid trace segments. Finally, traffic flow speed, congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level. The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level.http://html.rhhz.net/CHXB/html/2019-1-75.htmtraffic congestionsturning-levelspace time analysisGPS tracebig data |
collection |
DOAJ |
language |
zho |
format |
Article |
sources |
DOAJ |
author |
TANG Luliang KAN Zihan REN Chang ZHANG Xia LI Qingquan |
spellingShingle |
TANG Luliang KAN Zihan REN Chang ZHANG Xia LI Qingquan Fine-grained analysis of traffic congestions at the turning level using GPS traces Acta Geodaetica et Cartographica Sinica traffic congestions turning-level space time analysis GPS trace big data |
author_facet |
TANG Luliang KAN Zihan REN Chang ZHANG Xia LI Qingquan |
author_sort |
TANG Luliang |
title |
Fine-grained analysis of traffic congestions at the turning level using GPS traces |
title_short |
Fine-grained analysis of traffic congestions at the turning level using GPS traces |
title_full |
Fine-grained analysis of traffic congestions at the turning level using GPS traces |
title_fullStr |
Fine-grained analysis of traffic congestions at the turning level using GPS traces |
title_full_unstemmed |
Fine-grained analysis of traffic congestions at the turning level using GPS traces |
title_sort |
fine-grained analysis of traffic congestions at the turning level using gps traces |
publisher |
Surveying and Mapping Press |
series |
Acta Geodaetica et Cartographica Sinica |
issn |
1001-1595 1001-1595 |
publishDate |
2018-01-01 |
description |
For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis' operating patterns and filtered valid traces. Then this approach detected traffic congestion traces of three different intensities:mild congestion, moderate congestion and serious congestion, based on analyzing traffic conditions from the filtered valid trace segments. Finally, traffic flow speed, congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level. The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level. |
topic |
traffic congestions turning-level space time analysis GPS trace big data |
url |
http://html.rhhz.net/CHXB/html/2019-1-75.htm |
work_keys_str_mv |
AT tangluliang finegrainedanalysisoftrafficcongestionsattheturninglevelusinggpstraces AT kanzihan finegrainedanalysisoftrafficcongestionsattheturninglevelusinggpstraces AT renchang finegrainedanalysisoftrafficcongestionsattheturninglevelusinggpstraces AT zhangxia finegrainedanalysisoftrafficcongestionsattheturninglevelusinggpstraces AT liqingquan finegrainedanalysisoftrafficcongestionsattheturninglevelusinggpstraces |
_version_ |
1725883823893250048 |