The proportion of older drivers has increased with the aging population. In order to improve the driving behavior and safety of older drivers, we aim to analyze behavior differences between older and younger drivers and then study an improvement strategy based on the older drivers’ behavioral characteristics. Older drivers’ behaviors can be enhanced through training, thereby improving driving safety. Simulated scenarios for behavior analysis and training are constructed for drivers who are recruited from the general driving population. Data on the drivers’ eye movement, physiological and psychological conditions, operation behavior, and vehicle status are collected and analyzed. The theory of planned behavior is adopted to construct a driving behavior enhancement training model for older drivers. Finally, a structural equation model is developed to comprehend the relationship between training level, driver characteristics, and traffic safety. The ability and speed of older drivers to obtain traffic information is worse than those of young and middle-aged drivers, and the vehicle control capability of older drivers has a larger volatility. The driving behavior training model can improve older drivers’ driving stability and safety, as follows: the positive effect of training on driving behavioral improvement is larger than the negative effect of aging; the negative effect of training level on dangerous driving tendency is larger than the positive effect of driver’s aging. The driving behavior of older drivers should be improved for the safety and stability of driving operations through the PNE (perceived-norm-execution) model. The relationship between training level, driving behavior characteristics, and traffic safety is discussed using the structural equation model, and results show that the training can improve the effect of the drivers’ age on the characteristics of driving behavior, and that older drivers tend to decrease dangerous driving tendencies.

The widespread adoption of electric public buses has a positive effect on energy conservation and emission reduction, which is significant for sustainable development. This study aims to assess the safety and economy of electric buses based on drivers’ behavior. To this end, based on the remotely acquired travel data of buses, the driving operation behavior is analyzed, and four safety and four economic characteristic indicators are respectively extracted via safety analysis, correlation examination, and an R² test. Then, the extreme learning machine (ELM) is leveraged to establish the safety evaluation model, and Elman neural network is employed to construct the economic evaluation model. A comparative analysis and a comprehensive evaluation are conducted for the behaviors of ten drivers. The results highlight that the proposed evaluation model that us based on the ELM and Elman neural network algorithm can efficiently distinguish the safety and economy of driving behavior. Furthermore, a graph of driving operation behavior is constructed and the analysis results also manifest that the change of driving operation behavior of buses with higher safety and economy lead to relatively stable characteristics. When the fluctuation of vehicle speed is smooth, the driver can implement moderate driving operation in real-time. One critical conclusion that was revealed through the study is that there exists a certain correlation between driving safety and economy, and buses with higher safety tend to be more economical. This study can provide a theoretical basis for planning the maneuvering and operation of electric buses, including driving speed, braking, and acceleration operations.