Research On Resilience Assessment And Optimization Of Multi-Layer Bus-Metro Network Under Large Passenger Flow Interference

Public trams(hereinafter referred to as "buses")and the metro are two important means of public transportation in the city,which have made outstanding contributions to the sustainable development of urban transport.However,the large passenger flow caused by the surge in travel demand may lead to the failure of some stations and even the loss of transportation capacity of the entire bus and metro network.When bus and metro networks are disturbed by large passenger flow,it becomes a key issue for operational management departments to know how to use limited resources to restore the damaged network and improve the ability to cope with large passenger flow events.Most of the existing studies are based on a single bus or metro network.To improve the resilience assessment and optimization of bus and metro networks,this paper constructs a multi-layer bus-metro network model,analyzes the influence of different passenger flow interference scenarios on the resilience of the multi-layer bus-metro network,and studies the differences in the resilience optimization results of the multi-layer bus-metro network after implementing different recovery strategies.The details are as follows:Firstly,we propose a method to construct a multi-layer bus-metro network model.This method takes into account the composition and operation characteristics of bus and metro networks,considers the characteristic differences of each metro line,the mapping relationship between metro stations and bus stations,and the heterogeneity between transfer channels.The proposed multi-layer bus-metro network modeling process involves three steps: data collection and processing,adding edge connections,and network model construction.We analyze the structural characteristics of the multi-layer bus-metro network from three perspectives: stations,sections,and network layers.Secondly,we propose a resilience assessment method for a multi-layer bus-metro network under large passenger flow disturbance.To do so,we first divide the large passenger flow interference scene according to the characteristics of passenger flow.Then,we address the shortcomings of the load capacity model and the coupled map lattice model by redefining the initial state of the station,the coupling coefficient between stations,and the passenger flow transfer rule between stations,and constructing a coupled map lattice model based on the congestion propagation mechanism.We propose resilience assessment methods for the degradation stage,recovery stage,and the whole process,based on the resilience triangle and the time-dependent recovery loss ratio.From the perspective of passengers and operation,we propose passenger travel efficiency ratio and network service efficiency indicators,respectively.Finally,we determine the comprehensive weights of the indicators using the Delphi method,the entropy weight method,and the game theory comprehensive weighting method.Next,we propose a resilience optimization method for a multi-layer bus-metro network based on the resilience curve.To achieve this,we construct an optimal recovery strategy timing optimization model that combines recovery methods in the field of complex networks.The model aims to maximize the resilience of the multi-layer bus-metro network,subject to constraints of recovery time and resources.We use an adaptive genetic algorithm to solve the model and compare it with the random recovery strategy,the betweenness-based priority recovery strategy,and the efficiency-based priority recovery strategy.Finally,an empirical study was carried out by taking Xi ’an multi-layer bus-metro network as an example.Based on the bus and metro network data of Xi ’an city,this paper constructs a multi-layer bus-metro network model,analyzes the spatial and temporal distribution characteristics of weekday passenger flow in Xi ’an metro,and proposes four passenger flow interference scenarios: morning peak entry,morning peak exit,evening peak entry and evening peak exit.To assessment the network resilience changes of Xi’an multi-layer bus-metro network in the degradation stage,recovery stage and whole process under four scenarios of large passenger flow disturbance.The results show that the number of failure stations is directly proportional to the loss of network resilience from the perspective of resilience loss.From the perspective of the recovery strategy,the optimal recovery strategy based on the adaptive genetic algorithm can obtain the optimal recovery scheme,which can maximally reduce the loss of network resilience of the multi-layer bus-metro network after the interference of large passenger flow.From the perspective of recovery resources,appropriately increasing recovery resources can greatly improve network resilience,but the investment of resources is not proportional to the improvement of network resilience.In the four scenarios,when the number of failure stations reaches the maximum,the network performance drops to the lowest point.With the intervention of the assessment team,the network comprehensive performance gradually recovers to the initial network comprehensive performance state,but the network resilience cannot recover to the initial network resilience.This paper presents an in-depth study on the assessment and optimization of the resilience of a multi-layer bus-metro network.The results of this study can serve as a basis for bus and metro operation management departments to develop precise station management plans,ultimately improving the resilience of their networks.