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Edge computing technology is an important computer operating system in China. It plays a key role in multi-system fusion and intelligent manufacturing, and can play a key role in training and testing of deep neural networks. The purpose of this paper is to study the application of edge computing technology in the collaborative optimization of intelligent transportation systems based on information and physical fusion. This article sets up monitoring points at different traffic intersections, and applies long-term and short-term memory networks to collect data at each traffic intersection. The DBN-SVR method model was used to detect the traffic flow of some intersections, and the edge computer technology was used to process the information signals generated by the intersections. The other portions of the intersections used traditional monitoring systems. By comparing the work efficiency and utility under the two methods, fitting data is performed, and mathematical statistics and mathematical analysis methods are used to verify the fitted data. The experimental data show that the edge computing technology can help the processing of traffic conditions in the intelligent transportation system integrated with information and physics, which has greatly improved the overall work efficiency of each system. Experimental data shows that intelligent transportation systems that integrate edge computing technology with information physics have improved transportation efficiency by about 20% and urban security by about 35%, which has a great effect on building smart cities and safe cities.

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The Application of Edge Computing Technology in the Collaborative Optimization of Intelligent Transportation System Based on Information Physical Fusion