Research On Evacuation Optimization Of Sudden Large Passenger Flow In Resilient Transportation Hub

As an important part of the transportation system,transportation hubs connect a variety of transportation modes.Due to the functional characteristics of transportation hubs where passengers collect,distribute,and transfer,it always incurs a large gathering of passengers.The sudden large passenger flow appears especially on holidays or emergencies,which possibly causes passenger congestion,trampling,and even results in casualties and economic losses.Therefore,it is pivotal to improve the resilience of transportation hubs to deal with the pressure of sudden large passenger flow on the transportation system.The study on the optimization of sudden large passenger flow evacuation in resilient transportation hubs can provide an important theoretical and practical basis for the internal planning and layout of transportation hubs and passenger flow evacuation.The resilient characteristics of transportation hubs are studied from two aspects:considering the channel capacity expansion and opening emergency channels in resilient transportation hubs.The passenger flow between each origin and destination is pre-given,and the building structure and internal facility layout of resilient transportation hubs are analyzed to determine the potential passenger flow evacuation paths.To minimize the generalized cost,the optimization models of sudden large passenger flow evacuation in resilient transportation hubs considering the channel capacity expansion and opening of emergency channels are presented respectively.Then the chaos theory is introduced into the ant colony algorithm to solve the proposed models.The performance of the models and algorithm is verified through a small-scale example and a practical case,and the near optimal schemes of sudden large passenger flow in resilient traffic hubs were obtained.The main contents of this paper include the following parts:(1)From the perspective of adjusting channel capacity,an optimization model of sudden large passenger flow evacuation considering channel capacity expansion is proposed.The building structure and internal facility layout of resilient transportation hubs are analyzed to determine the potential passenger flow evacuation paths.In case of sudden large passenger flow,the capacity of the designated channels is considered to expand,and a passenger flow evacuation optimization model to minimize the generalized cost including passenger flow evacuation time and channel capacity expansion cost is established.To solve the model,the chaotic ant colony algorithm is presented.Finally,a small-scale case study is implemented to verify the effectiveness of the model and algorithm.The results show that adjusting channel capacity can effectively improve the efficiency of passenger flow evacuation,and the performance of chaotic ant colony algorithm is better than that of traditional ant colony algorithm.(2)From the perspective of changing the passenger evacuation paths,an optimization model of sudden large passenger evacuation considering opening the emergency channels is proposed.Considering the emergency channels that are not open at ordinary times can be opened for evacuating passenger flow when there is a sudden large passenger flow.The passenger flow between each origin and destination and the potential passenger flow evacuation paths are pre-given.Then a passenger flow evacuation optimization model is presented to minimize the generalized cost including the passenger flow evacuation time and the cost of opening the emergency channels.The chaotic ant colony algorithm is given to solve the model and obtain the near optimal passenger flow evacuation scheme.Finally,a real-world case is implemented with Beijing South Railway Station as the background.The results show that in case of sudden large passenger flow,opening emergency channels with appropriate locations can effectively reduce the evacuation time of passenger flow,and provide a theoretical basis for improving the resilience of transportation hubs.