The optimization of passenger evacuation path in subway station under emergencies directly affects the evacuation efficiency. Aiming at the evacuation scenario of passengers in subway station under multiple fires, a path planning method based on multi-objective robust optimization is established in this paper. The shortest total evacuation time, the minimum total risk and the minimum total congestion cost are taken as the objectives where the robustness of travel time and risk is also considered. NSGA-II algorithm is used to solve the model, the optimal Pareto solution under a certain robust control parameter is obtained according to the principle of minimizing the total cost function, and the overall optimization degree of route is obtained by using the evaluation index. Taking Qingdao May 4th Square subway station of China as a verification example, a fire diffusion model is built by using Pyrosim, and a passenger evacuation system is built by using Pathfinder. The multi-objective robust path optimization model of passenger evacuation is applied to the built passenger evacuation system under fires. The simulation results show that the overall optimization degree of route can reach 12.8 $\%$ , when the time and risk robust control parameters are 30 and 30, respectively. The method can be used to guide the passenger evacuation in subway stations under multiple hazard sources and improve the safety index of passenger travel.

As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

Reasonable planning of passenger evacuation routes can directly improve evacuation efficiency in the metro station. Considering the effect of traffic capacity at the node such as gate and stair/escalator, a multi-objective optimization model aimed at minimizing the maximum evacuation time of heterogeneous passengers in different segmentation areas is established, which achieves local optimization on the basis of global optimization of the evacuation route. Node traffic capacity is assessed based on the combination of BP neural network and atomic orbital search algorithm, which is conducive to improving the calculation accuracy of total evacuation time. The simulation results indicate that the performance of the established prediction model of node travel time is good, and the mean square error can be as low as 0.2555. Meanwhile, the proposed route strategy can improve the overall evacuation efficiency by 21.85%, and passengers’ choices of evacuation routes are more reasonable. The sensitivity analysis of factors affecting evacuation through a random forest algorithm shows that the number of passengers with person attribute E has the greatest impact on evacuation time, which should be the focus of attention in the evacuation process.

In order to achieve good connectivity after the cascading failure of a logistics network, this paper studies the controllability robustness of complex logistics network based on the nonlinear load-capacity (NLC) model. Firstly, the extended Baraba′si and Albert (BA) network is constructed as a complex logistics network for experiments, based on the power law distribution and the agglomeration and sprawl evolution mechanism. Secondly, the existence of the NLC relationship of the real logistics network is proved, and then the NLC model of complex logistics networks is proposed. Furthermore, a simulation analysis of the controllability robustness and influencing factors of the complex logistics network is carried out under four different cascading failure models. In those models, different scenarios of the NLC and the classical linear load-capacity (LLC) model with initial load (IL)/initial residual capacity (IRC) loadredistribution strategies are combined. The research results show that the main influencing factors of the cascading failure of complex logistics networks for the controllability robustness i P are the tolerance parameters β and γ. Moreover, the effect of γ on the load-capacity relationship under the NLC model is more significant than that of β . Among the four cascading failure models, the one based on the NLC model with IRC strategy is the optimal for controllability robustness. Based on the optimal model, the simulation considering the perspective of the logistics economy shows that the relationship among the network cost e , P i and γ is as follows: under a fixed cost, the greater is γ, the stronger is P i. Also, when 2< γ ≤9, the robustness of the network is controllable. According to the requirements of real logistics networks, both controllability robustness and the logistics cost can be controlled, and a solution that against cascading failure can be obtained by adjusting the minimum residual load.

Based on relevant complex network theory, this paper analyzes the characterization parameters of traffic network complexity, such as the causes of traffic congestion and evacuation models. From the perspective of the traffic capacity of traffic network nodes, combined with the transit time of each road, model is proposed for selecting the weights of congestion evacuation nodes. According to the evacuation target, combined with characteristic parameters, such as node degree, node strength, clustering coefficient and closeness, the grey system evaluation method and the analytic hierarchy process (AHP)are combined. Based on the grey relational analysis model, a model is established for determining the priority connectivity evaluation value of each node. The complex characteristics of the actual traffic network in the Chaoyang District of Changchun City are analyzed, then the selection weights of each node of the traffic network are obtained. Having defined the distance between complex network nodes, a congestion evacuation path selection model is proposed, and an evacuation path scheme is given for specified start and end points.

Inspired by the empirical dynamic characteristics of the load of real logistics network, we propose a dynamic cascading failure model against cascading failure, which is more suitable for complex logistics network by adding dynamic factors based on the nonlinear load-capacity model under initial residual capacity load-redistribution strategy. The simulation is executed on the complex logistics network model and the results show that the controllability robustness and economy after cascading failure based on the dynamic cascading failure model is feasible and effective. It can effectively reduce the logistics cost and enhance controllability robustness against cascading failure by adjusting the network cost $e$ and capacity parameter $\gamma $ , so as to balance the controllability robustness and economy for the complex logistics network.