Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China
In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In...
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sg-ntu-dr.10356-1460352021-01-21T08:45:35Z Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China Jia, Jianlin Chen, Yanyan Chen, Ning Yao, Hui Li, Yongxing Liu, Zhuo School of Civil and Environmental Engineering Engineering::Civil engineering Transportation Networks Passenger Flow In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed. Published version 2021-01-21T08:45:35Z 2021-01-21T08:45:35Z 2020 Journal Article Jia, J., Chen, Y., Chen, N., Yao, H., Li, Y., & Liu, Z. (2020). Identifying Key Bus Stations Based on Complex Network Theory considering the Hybrid Influence and Passenger Flow: A Case Study of Beijing, China. Advances in Civil Engineering, 2020, 8824797-. doi:10.1155/2020/8824797 1687-8086 0000-0002-4033-1934 0000-0002-7068-4669 0000-0002-3786-8730 https://hdl.handle.net/10356/146035 10.1155/2020/8824797 2-s2.0-85098505924 2020 en Advances in Civil Engineering © 2020 Jianlin Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. application/pdf |
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Engineering::Civil engineering Transportation Networks Passenger Flow Jia, Jianlin Chen, Yanyan Chen, Ning Yao, Hui Li, Yongxing Liu, Zhuo Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
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In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Jia, Jianlin Chen, Yanyan Chen, Ning Yao, Hui Li, Yongxing Liu, Zhuo |
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Article |
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Jia, Jianlin Chen, Yanyan Chen, Ning Yao, Hui Li, Yongxing Liu, Zhuo |
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Jia, Jianlin |
title |
Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
title_short |
Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
title_full |
Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
title_fullStr |
Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
title_full_unstemmed |
Identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of Beijing, China |
title_sort |
identifying key bus stations based on complex network theory considering the hybrid influence and passenger flow : a case study of beijing, china |
publishDate |
2021 |
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https://hdl.handle.net/10356/146035 |
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1690658409968304128 |