TY - JOUR
T1 - Stability of Beijing rail transit network under the impact of large passenger flow
AU - Sun, Li Shan
AU - Huang, Yu Chen
AU - Gong, Qing Sheng
AU - Zhang, Tian Qi
AU - Yao, Li Ya
N1 - Publisher Copyright:
© 2018, Editorial Department of Journal of Chang'an University (Natural Science Edition). All right reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In order to reduce the operation instability of rail transit station, optimize the structure of rail transit network and improve the efficiency of network operation, the stability of rail transit operation under the impact of large passenger flow was studied. Taking Beijing rail transit network as an example, Beijing rail transit network model with rail stations as nodes and adjacent stations as edges was constructed by using Space L method. The characteristic parameters and distribution rules of node degree, average path length, clustering coefficient, network diameter and network efficiency were simulated and analyzed. According to the type of accidents, these stations were divided into site operation weakened and operation disrupted, respectively. Base on passenger-flow propagation model and network efficiency model, the simulation quantified the network stability under the background of different event types. The results show that Beijing rail transit network is a scale-free network. The accessibility of rail transit network is better and clustering coefficient of it is small, but connectivity of it should be improved. And when site operation is weakened because of the large passenger flow in Beijing transit network, if the number of adjacent station is not more than 7, passenger-flow propagation state in network can regress, or negative correlation is observed between the number of adjacent station and the fading time. When transfer station is disrupted, the value of network efficiency will decrease significantly. The importance coefficient of the damaged site has the greatest impact on network efficiency in the range of (0.75, 1], and the least in the range of (0, 0. 5]. The research results provide a basis for timely finding the key points and weak links of rail network, ensuring the safety operation of urban rail transit network under the background of super passenger flow, and providing a decision support for the development of emergency plan of Beijing rail transit network under the impact of large passenger flow. 5 tabs, 5 figs, 16 refs.
AB - In order to reduce the operation instability of rail transit station, optimize the structure of rail transit network and improve the efficiency of network operation, the stability of rail transit operation under the impact of large passenger flow was studied. Taking Beijing rail transit network as an example, Beijing rail transit network model with rail stations as nodes and adjacent stations as edges was constructed by using Space L method. The characteristic parameters and distribution rules of node degree, average path length, clustering coefficient, network diameter and network efficiency were simulated and analyzed. According to the type of accidents, these stations were divided into site operation weakened and operation disrupted, respectively. Base on passenger-flow propagation model and network efficiency model, the simulation quantified the network stability under the background of different event types. The results show that Beijing rail transit network is a scale-free network. The accessibility of rail transit network is better and clustering coefficient of it is small, but connectivity of it should be improved. And when site operation is weakened because of the large passenger flow in Beijing transit network, if the number of adjacent station is not more than 7, passenger-flow propagation state in network can regress, or negative correlation is observed between the number of adjacent station and the fading time. When transfer station is disrupted, the value of network efficiency will decrease significantly. The importance coefficient of the damaged site has the greatest impact on network efficiency in the range of (0.75, 1], and the least in the range of (0, 0. 5]. The research results provide a basis for timely finding the key points and weak links of rail network, ensuring the safety operation of urban rail transit network under the background of super passenger flow, and providing a decision support for the development of emergency plan of Beijing rail transit network under the impact of large passenger flow. 5 tabs, 5 figs, 16 refs.
KW - Complex network
KW - Network efficiency model
KW - Network stability
KW - Passenger-flow propagation model
KW - Rail transit
KW - Traffic engineering
UR - https://www.scopus.com/pages/publications/85050653182
M3 - Article
AN - SCOPUS:85050653182
SN - 1671-8879
VL - 38
SP - 98-104 and 126
JO - Chang'an Daxue Xuebao (Ziran Kexue Ban)/Journal of Chang'an University (Natural Science Edition)
JF - Chang'an Daxue Xuebao (Ziran Kexue Ban)/Journal of Chang'an University (Natural Science Edition)
IS - 1
ER -