For the complicated operation process, many risk factors, and long cycle of urban logistics, it is difficult to manage the security of urban logistics and it enhances the risk. Therefore, to study a set of effective management mode for the safe operation of urban logistics and improve the risk prediction mechanism, is the primary research item of urban logistics security management. This paper summarizes the risk factors to public security in the process of urban logistics, including pick up, warehouse storage, transport, and the end distribution. Generalized regression neural network (GRNN) is combined with particle swarm optimization (PSO) to predict accidents, and the Apriori algorithm is used to analyze the combination of high-frequency risk factors. The results show that the method of combining GRNN with PSO is effective in accident prediction and has a powerful generalization ability. It can prevent the occurrence of unnecessary urban logistics public accidents, improve the ability of relevant departments to deal with emergency incidents, and minimize the impact of urban logistics accidents on social and public security.

Due to the uncertainty and complexity of multilinks and multifactors in urban express logistics system, it is very difficult to analyze the risk factors and the correlation among them for urban public security. In this paper, a method combining domain knowledge and data learning is proposed to construct Bayesian network, which can effectively deal with this problem. Based on the literature review and the investigation of transportation companies, this paper summarizes the risk factors to public safety caused by pick up, warehouse storage, transport, and the end distribution in the process of urban express logistics, which are divided into 5 dimensions: management, weather, human, transportation tools and facilities, and goods, including 11 risk factors. In this paper, Interpretative Structural Model is used to construct the initial hierarchical model to describe the complex relationship between factors, and then causal mapping method is used to improve the initial model to transform the structure into the final Bayesian network model. Finally, the sensitivity of one node to other nodes is analyzed based on the incident data. The results show that Bayesian network is effective in improving urban express logistics operation ability and avoiding public safety risks and has a strong generalization ability, which is simple and easy in practical application.

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The coordination of different container-handling equipment is an important method for improving the overall efficiency of automated container terminals. In the real terminal, we should consider many real-life issues, such as the equipment capacity, the equipment collision, changing lanes in the multi-lane road, and choosing one of container-handling lanes for each container. This paper proposes the integrated scheduling problem of three container-handling equipment with the capacity constraint and the dual-cycle strategy, for simultaneously solving the equipment scheduling problem, the assignment problem of the container-handling lane and the conflict-free route planning problem of automated guided vehicles (AGVs). With the objective of minimizing the ship’s berth time, we propose a mixed-integer programming model based on the space-time network representation method and two bilevel optimization algorithms based on conflict resolution rules. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and two bilevel optimization algorithms.