School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China.
PLoS One. 2020 Oct 5;15(10):e0238443. doi: 10.1371/journal.pone.0238443. eCollection 2020.
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.
对于复杂的操作过程、众多风险因素以及城市物流的长周期而言，城市物流的安全管理难度较大，风险也随之增加。因此，研究一套有效的城市物流安全运行管理模式，完善风险预测机制，是城市物流安全管理的首要研究课题。本文总结了城市物流过程中对公共安全造成影响的风险因素，包括取货、仓储、运输和末端配送。将广义回归神经网络（GRNN）与粒子群优化（PSO）相结合，对事故进行预测，并采用 Apriori 算法对高频风险因素的组合进行分析。结果表明，GRNN 与 PSO 相结合的方法在事故预测方面非常有效，具有强大的泛化能力。该方法可以防止不必要的城市物流公共事故的发生，提高相关部门应对突发事件的能力，将城市物流事故对社会和公共安全的影响降到最低。
