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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8824797 |
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| _version_ | 1832567840945733632 |
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| author | Jianlin Jia Yanyan Chen Ning Chen Hui Yao Yongxing Li Zhuo Liu |
| author_facet | Jianlin Jia Yanyan Chen Ning Chen Hui Yao Yongxing Li Zhuo Liu |
| author_sort | Jianlin Jia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-038d4062c4c94fdab4d63dcc78c7595c |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-038d4062c4c94fdab4d63dcc78c7595c2025-02-03T01:00:20ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88247978824797Identifying Key Bus Stations Based on Complex Network Theory considering the Hybrid Influence and Passenger Flow: A Case Study of Beijing, ChinaJianlin Jia0Yanyan Chen1Ning Chen2Hui Yao3Yongxing Li4Zhuo Liu5Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaSchool of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, SingaporeBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaIn 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.http://dx.doi.org/10.1155/2020/8824797 |
| spellingShingle | Jianlin Jia Yanyan Chen Ning Chen Hui Yao Yongxing Li Zhuo Liu 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 |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2020/8824797 |
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