Abstract
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.
Highlights
Taniguchi et al [1] defines urban logistics as "the process by which private enterprises achieve the overall optimization of logistics and transportation activities in the market economy, taking into account the urban traffic environment, traffic congestion, and energy consumption".The city is the concentration of logistics activities, the logistics activities in the city are the most important part of the whole logistics link
BP neural network, Generalized regression neural network (GRNN), and particle swarm optimization (PSO)-GRNN are used to predict the accident level, and the results show that PSO-GRNN can effectively improve the accuracy of accident level prediction reaching 80%, which provides a new idea for the study of urban logistics safety management and has a good reference significance
This paper introduces a method of public security risk prediction and risk factor analysis of urban logistics
Summary
OPEN ACCESS Citation: Zhao M, Ji S, Wei Z (2020) Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm. 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. 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.
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