摘要
This study aims to investigate the relationship between the Multiple Centrality Assessment index and the level of traffic congestion using the data from the urban traffic networks in Harbin, China. By utilizing the standard 'primal' format in the traffic networks, the street centrality can be measured by three types of indices, the Closeness, Straightness and Betweenness centralities. These centrality indices were calculated by Python program based on Arcgis10.1. The correlations between congestion level and the street centralities were analyzed based on the collected traffic data on the top ten congested traffic links on weekdays in urban areas. The results indicated that the street centrality indices and congestion level are positively related. Among three centrality indices, the global betweenness exhibited a higher correlation with the congestion time delay index than the global closeness did. The study demonstrated a new aspect to investigate traffic congestion and provided useful information for transportation planning.
| 原文 | English |
|---|---|
| 主出版物標題 | Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015 |
| 主出版物子標題 | Urban Transport Analytics |
| 編輯 | Sylvia Y. He, Yong-Hong Kuo, C.H. Cheng, Janny M.Y. Leung |
| 發行者 | Hong Kong Society for Transportation Studies Limited |
| 頁面 | 429-436 |
| 頁數 | 8 |
| ISBN（電子） | 9789881581440 |
| 出版狀態 | Published - 2015 一月 1 |
| 事件 | 20th International Conference of Hong Kong Society for Transportation Studies: Urban Transport Analytics, HKSTS 2015 - Hong Kong, Hong Kong 持續時間: 2015 十二月 12 → 2015 十二月 14 |
出版系列
| 名字 | Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015: Urban Transport Analytics |
|---|
Other
| Other | 20th International Conference of Hong Kong Society for Transportation Studies: Urban Transport Analytics, HKSTS 2015 |
|---|---|
| 國家 | Hong Kong |
| 城市 | Hong Kong |
| 期間 | 15-12-12 → 15-12-14 |
指紋
All Science Journal Classification (ASJC) codes
- Transportation
引用此文
}
Investigating the relationship between street centrality and traffic congestion. / Li, Jingsi; Lee, Tzu-Chang; Pan, Runqiu.
Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015: Urban Transport Analytics. 編輯 / Sylvia Y. He; Yong-Hong Kuo; C.H. Cheng; Janny M.Y. Leung. Hong Kong Society for Transportation Studies Limited, 2015. p. 429-436 (Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015: Urban Transport Analytics).研究成果: Conference contribution
TY - GEN
T1 - Investigating the relationship between street centrality and traffic congestion
AU - Li, Jingsi
AU - Lee, Tzu-Chang
AU - Pan, Runqiu
PY - 2015/1/1
Y1 - 2015/1/1
N2 - This study aims to investigate the relationship between the Multiple Centrality Assessment index and the level of traffic congestion using the data from the urban traffic networks in Harbin, China. By utilizing the standard 'primal' format in the traffic networks, the street centrality can be measured by three types of indices, the Closeness, Straightness and Betweenness centralities. These centrality indices were calculated by Python program based on Arcgis10.1. The correlations between congestion level and the street centralities were analyzed based on the collected traffic data on the top ten congested traffic links on weekdays in urban areas. The results indicated that the street centrality indices and congestion level are positively related. Among three centrality indices, the global betweenness exhibited a higher correlation with the congestion time delay index than the global closeness did. The study demonstrated a new aspect to investigate traffic congestion and provided useful information for transportation planning.
AB - This study aims to investigate the relationship between the Multiple Centrality Assessment index and the level of traffic congestion using the data from the urban traffic networks in Harbin, China. By utilizing the standard 'primal' format in the traffic networks, the street centrality can be measured by three types of indices, the Closeness, Straightness and Betweenness centralities. These centrality indices were calculated by Python program based on Arcgis10.1. The correlations between congestion level and the street centralities were analyzed based on the collected traffic data on the top ten congested traffic links on weekdays in urban areas. The results indicated that the street centrality indices and congestion level are positively related. Among three centrality indices, the global betweenness exhibited a higher correlation with the congestion time delay index than the global closeness did. The study demonstrated a new aspect to investigate traffic congestion and provided useful information for transportation planning.
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UR - http://www.scopus.com/inward/citedby.url?scp=84964330475&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84964330475
T3 - Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015: Urban Transport Analytics
SP - 429
EP - 436
BT - Proceedings of the 20th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2015
A2 - He, Sylvia Y.
A2 - Kuo, Yong-Hong
A2 - Cheng, C.H.
A2 - Leung, Janny M.Y.
PB - Hong Kong Society for Transportation Studies Limited
ER -