Vehicle–road coordination is an important application scenario in the sustainable development of urban transportation. In this scenario, by navigating the vehicles in the road network, the vehicles can run more smoothly in the city, reduce unnecessary detours and parking, and realize energy savings and emission reductions. Although vehicle–road coordination in a large area has not been fully realized, people’s travel is increasingly dependent on navigation. If the trips of most vehicles follow the same navigation suggestion in a short period of time, some sections in the given route of the navigation will bear excessive traffic load. In order to solve this potential problem, this paper relies on the vehicle–road collaboration scenario and combines the service level of the road network factors between vehicles to plan the travel path of the vehicle. This keeps the traffic load of each road section in the path at a reasonable level. Within the scope, considering the overall utilization of road resources and the efficiency of road network traffic, we established the road network evaluation index through the simulation comparison with the Dijkstra algorithm. Under the path planning method proposed in this paper, the total travel time of the vehicle is reduced by 23.4%, and the road network operation efficiency is improved by 6.6%, which proves that the method can be used. This method can effectively alleviate the load of the road network, improve operation efficiency, and finally achieve the purpose of energy saving and emission reduction.

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With the increase in people’s travel demands, the air pollution generated by the means of transportation they take is also becoming more and more serious. Among them, in the process of people’s travel, the exhaust pollution caused by traffic congestion is particularly serious. Accurately identifying various regimes of oversaturation and taking effective control strategies play a key role in alleviating traffic congestion. There are three regimes of evolution during an oversaturated scenario: loading, oversaturated operation, and recovery. In the traffic signal control under the oversaturated scenario, the corresponding control targets and methods should be adopted based on the regime of oversaturation. In this paper, the multi-objective attributes and their trajectory data of each movement at the intersection are analyzed. Based on the oversaturation severity index, the traffic volume, and the queuing on the movement, the identification and cause analysis of each regime of the oversaturation are carried out. The examples and simulation results proved that the method proposed in this paper could effectively analyze the cause and degree of oversaturation and identify its regime. This has important implications for alleviating traffic congestion and reducing vehicle carbon emissions.