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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Main Authors: TANG Luliang, KAN Zihan, REN Chang, ZHANG Xia, LI Qingquan
Format: Article
Language:zho
Published: Surveying and Mapping Press 2018-01-01
Series:Acta Geodaetica et Cartographica Sinica
Subjects:
Online Access:http://html.rhhz.net/CHXB/html/2019-1-75.htm
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spelling 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
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