Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data
With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environm...
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| Format: | Article |
| Language: | English |
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University of Minnesota Libraries Publishing
2017-06-01
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| Series: | Journal of Transport and Land Use |
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| Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/954 |
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| author | Kaisheng Zhang Daniel (Jian) Sun Suwan Shen Yi Zhu |
| author_facet | Kaisheng Zhang Daniel (Jian) Sun Suwan Shen Yi Zhu |
| author_sort | Kaisheng Zhang |
| collection | DOAJ |
| description | With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on 24-hour congestion pattern of road segments in urban area, so that the spatial autoregressive moving average model (SARMA) was introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained 12 traffic-related factors and land use factors’ impact on road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use and so on have large impact on congestion formation. The Fuzzy C-means clustering was proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service of traffic from the congestion perspective. |
| format | Article |
| id | doaj-art-94a84955a3f04579b19ebeafce619cd9 |
| institution | DOAJ |
| issn | 1938-7849 |
| language | English |
| publishDate | 2017-06-01 |
| publisher | University of Minnesota Libraries Publishing |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj-art-94a84955a3f04579b19ebeafce619cd92025-08-20T03:19:47ZengUniversity of Minnesota Libraries PublishingJournal of Transport and Land Use1938-78492017-06-0110110.5198/jtlu.2017.954267Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS dataKaisheng Zhang0Daniel (Jian) Sun1Suwan Shen2Yi Zhu3Shanghai Jiao Tong UniversityShanghai Jiao Tong UniversityUniversity of Hawaii, ManoaShanghai Jiao Tong UniversityWith the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on 24-hour congestion pattern of road segments in urban area, so that the spatial autoregressive moving average model (SARMA) was introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained 12 traffic-related factors and land use factors’ impact on road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use and so on have large impact on congestion formation. The Fuzzy C-means clustering was proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service of traffic from the congestion perspective.https://www.jtlu.org/index.php/jtlu/article/view/954Congestion PatternTaxi GPS DataFuzzy C-means ClusterSpatiotemporal RegressionBuilt Environment Factor |
| spellingShingle | Kaisheng Zhang Daniel (Jian) Sun Suwan Shen Yi Zhu Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data Journal of Transport and Land Use Congestion Pattern Taxi GPS Data Fuzzy C-means Cluster Spatiotemporal Regression Built Environment Factor |
| title | Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data |
| title_full | Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data |
| title_fullStr | Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data |
| title_full_unstemmed | Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data |
| title_short | Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data |
| title_sort | analyzing spatiotemporal congestion pattern on urban roads based on taxi gps data |
| topic | Congestion Pattern Taxi GPS Data Fuzzy C-means Cluster Spatiotemporal Regression Built Environment Factor |
| url | https://www.jtlu.org/index.php/jtlu/article/view/954 |
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