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.

With the rapid economic development, parking problems have become increasingly prominent due to the city's development model and the emergence of a large number of private cars. Parking management departments around the world focus on intelligent parking system in order to solve parking problems, but most of them are limited to upgrading the parking infrastructure. There is no effective solution from a perspective of the fundamental cause to solve the parking problem. At present, it is generally believed that parking guidance systems can effectively alleviate parking problems and provide drivers and traffic managers with real-time and accurate parking information. As one of the prerequisites of the parking guidance system, the accuracy of the short-term parking demand prediction method determines whether the implementation of the parking guidance scheme can effectively solve the parking problem in a certain area. Based on this, we have studied a short-term parking demand prediction in this paper. First, focusing on the distribution of typical parking arrivals and departures regular pattern, a parking demand prediction model was constructed utilizing the Markov birth and death process, and model parameters were calibrated utilizing curve fitting method and undetermined coefficients method. The simulation environment was set up utilizing the Markov process to verify the accuracy of the availability of parameter estimation method. Secondly, a method for determining the prediction interval based on the parking trend was given for different situations with the parking rush hours and ordinary hours which parking arrival and departure parameters were different. In order to verify the effectiveness of the model in practical applications, parking arrival and departure data from June 17 to June 23, 2019 in Jilin University of Nanling Campus was used to verify the short-term parking demand prediction method proposed in this paper. The results show that the parking demand prediction model proposed in this paper can accurately calibrate model parameters, predict parking demand quickly and effectively and provide theoretical reference and technical support for parking planning and management.

The networked operation of Urban Rail Transit (URT) brings the new challenge of network-wide maintenance. This research focuses on the URT Network-Wide Maintenance Decision-Making Problem (URT-NMDP), including regionalized maintenance network design and maintenance resource allocation. In this work, we proposed a bi-objective integer programming model that integrates the characteristics of set coverage and P-median models, resulting in the regionalized maintenance network design model. Some critical factors are considered in the model, such as the importance of node, the maximum failure response time, and maintenance guarantee rules. We designed a NSGA-II based algorithm to solve the model. Moreover, due to the uncertainty of failures in the URT network, we developed the method of allocating maintenance resources based on Monte Carlo simulation to strengthen the reliability of the regionalized maintenance network. With the model and algorithm presented in this work, we obtained Pareto optimal solutions of URT-NMDP, i.e., URT network-wide maintenance planning schemes, which include the number and location of maintenance points, the allocation of demand points, and the amount of maintenance units. Finally, a real-world case is studied to evaluate the operating performance of these schemes for verifying the method in our paper. The results of the case study demonstrate that the reasonable and tested-in-practice maximum failure response time is the precondition for the efficient URT maintenance network. The maintenance scheme considered the weighted importance of node shows the optimal performance, with the shortest overall maintenance path and the minimum average failure response time and investment cost on maintenance resources.