In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.

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.

In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.