This session discusses resilience of urban rail transit networks and models of metro station evacuatlon under emergency conditions.
Disposal model for mass passenger flow emergency at metro stations with train collaborative adjustment
Wei Li (firstname.lastname@example.org), Shenzhen Technology UniversityShow Abstract
Qin Luo, Shenzhen Technology University
To quickly evacuate the delayed passengers in emergencies, this paper proposes a metro train collaborative disposal method to adjust the stop schedule plan of some trains. The train remaining capacity can be maintained by nonstop passing some stations ahead to rapidly evacuate passengers when a mass passenger flow occurs. Taking the minimization of passengers’ average waiting time as the optimization objective, this paper establishes a reasonable train collaborative disposal model for trains with mass passenger flow. Then, the Q-learning algorithm is applied to solve the problem to achieve a rapid and reasonable train stop schedule plan. Finally, the event of mass passenger flow caused by a concert at Houhai station of Shenzhen Metro is used as a case study, and it is proven that the proposed model can shorten the average waiting time of passengers in the emergency. The proposed method can improve the evacuation efficiency of rail transit stations and ensure the normal operation order of the metro system when a mass passenger flow occurs.
Evacuation Demand Prediction under Metro Rail Disruptions Based on Normal Historical Data
Xiaoqing Dai, Ministry of Transport of the People’s Republic of ChinaShow Abstract
Han QIU, Meituan-Dianping Group
Lijun SUN, Tongji University
Historical data of metro rail passenger volumes under disruptions, such as line and station closure, is hard to collect, leading to the difficulty in predicting evacuation demand under metro disruptions and emergency response strategy design. It becomes the bottleneck of the emergency response as well. To fill this gap, we develop a method to predict evacuation demand under metro disruptions mainly using historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand of every type of metro station. Input variables in this step are features related to the station under normal state. Then based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of passenger demand within the whole network under disruptions. The metro capacity drop under disruptions is also used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to validate the proposed method. The results show that, our method could give the prediction of evacuation demand and its evolvement, as well model how severe stations will be affected by given disruptions. Our simulation results show that, the most vulnerable stations under disruption, which are the location where peak stranded passenger volume takes place, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the metro system disruption.
The Strategies to Enhance the Resilience of an Urban Rail Transit Network
Jinqu Chen, Southwest Jiaotong UniversityShow Abstract
Jie Liu, Southwest Jiaotong University
Qiyuan Peng, Southwest Jiaotong University
Yong Yin, Southwest Jiaotong University
As an important component of the urban infrastructure system, urban rail transit (URT) is extremely vulnerable to emergencies (such as natural disasters and terrorist attacks). Therefore, it is of great significance to build a high resilient URT network. The resilience enhancement models for normal operating and damaged URT network are discussed herein, respectively. Firstly, a bi-level programming model aiming at maximizing passenger’s global accessibility and network efficiency is formulated to improve the resilience of normal operating URT network. Secondly, the repair strategy considering the dynamic change of the URT structure is proposed to enhance the resilience of the damaged URT network caused by emergencies. Finally, the proposed models are applied to Chengdu subway network. The result indicates that the bi-level programming model guides to construct new links to enhance the resilience of Chengdu subway network when the network operates normally. The deliberate attack is more harmful to Chengdu subway network than random attack, operators should pay attention to the operation of key stations to improve the URT network’s ability to respond to emergencies. The repair strategy proposed here has a better repair effect than the conventional repair strategy, it can improve the damaged URT network’s resilience with the least repair resources and the highest repair efficiency.
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