Urban interchange is the core hub connecting various regions, and it is of great significance for alleviating the problem of traffic congestion. In the process of urban interchange design, it is impossible to strictly control the traffic volume, interchange types, and standards by relying on traditional technologies. Smart transportation and big data are emerging technologies based on data, which can provide technical support for design and decision making. Based on this, this paper first uses smart transportation and big data technology to predict the traffic volume of Nancheng New District, so as to calculate the future development trend of the target area. Then, on the basis of traffic volume, the article uses smart transportation and big data technology to optimize the original urban interchange design scheme from the aspects of traffic capacity, safety, economic benefits, and environmental benefits. Finally, the article evaluates the optimized urban interchange scheme by means of comprehensive quantitative indicators and evaluation methods. Experiments show that the traffic capacity of the interchange on the outer ring road optimized by smart transportation and big data has increased to 72.6%, and the environmental coordination has increased from 45.2% to 55.2%. Moreover, the design aesthetics of the urban interchange after optimized design based on smart transportation and big data has increased to 65.9%. In addition, the comprehensive evaluation value of the urban interchange after optimization of smart transportation and big data reached 82.6. This fully shows that the optimal design of urban interchange based on the integration of smart transportation and big data can greatly improve the traffic capacity of urban roads.

The development of the Internet of vehicles technology can improve the communication between vehicles, thereby changing the driving behavior of drivers. Therefore, the traditional safe-following model cannot accurately describe the driving behavior and needs to be improved accordingly. First, two key parameters (i.e., drivers’ reaction sensitivity and road friction coefficient) are obtained through a comprehensive comparative analysis of influencing factors on the Internet of vehicles environment. And the calculation methods of these two parameters are proposed by using the multilevel comprehensive weighted evaluation method and the BP neural network. Then, these two key parameters are used to improve the traditional minimum safety distance model for adapting to driving behavior under the Internet of vehicles environment. Finally, through setting up simulation experiments and comparative analysis, the relationship between different influencing factors and the minimum safe following distance is obtained, and the influence degree of different influencing factors is sorted. The most important factor affecting car-following safety is the drivers’ characteristics. It can provide strong theoretical support for the safe driving assistance system of vehicles.

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