The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects.

A chemical industry park (CIP) has a wide variety of hazardous chemicals, and once an accident occurs, the level of danger increases geometrically, while the domino effect may bring devastating consequences. To improve the emergency rescue capability of a chemical park and prevent the domino effect, a certain number of emergency centers are built at sites near the park for the purpose of rapid emergency rescue and deployment of emergency supplies. Based on this, in our study, a siting model of the emergency center of the chemical park, which aims to prevent the domino effect, was constructed by considering the timeliness and safety, while adopting the prevention of the domino effect as a constraint. The NSGA-II algorithm is used to solve the siting model, and the CPLEX method is used for the comparison. This study combines the prevention of the domino effect with multi-objective optimization theory, which has a good and simple applicability for solving the considered problem and can obtain solutions in line with science and reality. It also adds the risk radius of the demand point based on the traditional siting model and proposes a model that combines the risk and distance to reduce the risk of accidents across the whole region. Finally, the model is applied to a chemical park in China for an arithmetic analysis to provide decision makers with a targeted reference base for the siting of an emergency center. The experimental results show that the NSGA-II algorithm can effectively solve the model of the emergency center in the chemical park and outperforms the results obtained from the CPLEX solution in terms of its cost and safety.