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In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

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In order to improve the satisfaction of users during the human–machine interaction with intelligent electric vehicles, this paper presents the human–machine interaction method of intelligent electric vehicles. Firstly, the principle of human–computer interaction of intelligent electric vehicles is analyzed, the application of interaction in big data visualization is expounded, and the cognitive mechanism of big data visualization interaction is designed. According to the above mechanism, the design the of information interface and the HUD interface is completed, and the interaction model is established. So far, the design of a human–computer interaction method of intelligent electric vehicles is completed. The experimental results show that the human–computer interaction response time of the design method is was only 5 ms, and the human-computer interaction satisfaction of the intelligent electric vehicle can reach 99%, which has certain application value.

The stability of unmanned vehicle is related to the safety of the vehicle itself. In the process of unmanned vehicle control, there will be collision phenomenon in the process of meeting the vehicle. To solve the above problem, the design of unmanned interaction system based on visual cognition is proposed. The hardware structure of the system is designed based on 80C51 single chip microcomputer, including ARM processor, GPS receiving module, driving record signal collecting module, etc. The PID controller design based on neural network is optimized, and the design of unmanned interactive system based on visual cognition is completed. Experimental results show that the designed system can identify the surrounding environment in real time, make corresponding decisions, let the vehicle avoid the wrong vehicle operation, and save Oil consumption.