Considerable evidence suggests that the decline in physiological abilities prevalent in older drivers leads to a reduction in the visual and psychomotor functions required for safe driving. The purpose of this study is to further investigate the differences in driving behavior between older and younger drivers and to describe the change process of driving behavior. In this study, 19 younger and older drivers each were recruited for a driving simulation experiment that included five scenarios. Driving operation data, eye movement data, and physiological data of drivers in five conflict scenarios were collected. The differences in driving behaviors between the two groups were also compared and analyzed, on which the thresholds of different driving behavior nodes were determined and driving behavior graphs were established. The results show that the eye movement nodes of older drivers appear later in five scenarios, the operational nodes of older people appear later in two steering scenarios, and are closer to those of younger drivers in three straight ahead scenarios, indicating that older drivers were later in observing and collecting traffic information, and later in applying brakes and steering to avoid conflicts when steering. The study provides a reference for the analysis of driving behavior and driving safety of older people.

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

To investigate the effects of proactive safety control systems suitable for highway interchanges and improve road traffic safety. Simulated driving experiments were conducted to test the effects of the interchange warning system (IWS) on the ramp, merging section, diverging section, and accident section. Random forest (RF) and SHapley Additive exPlanations (SHAP) are used to analyze the effects between driving behavior and driving risk change in both situations without and with IWS. The results show that (1) as driving risk increases, drivers tend to increase the frequency of braking and engage in more comprehensive saccade behaviors. Concurrently, there is an increase in acceleration and speed variation, leading to a gradual decrease in speed. (2) Compared with the SVR and XGBoost, RF can better fit the nonlinear relationship between driving risk and driver behavior characteristics with the application of IWS. (3) The IWS mainly reduces driving risk by affecting operation behavior. When the mean speed, speed standard deviation (SD), acceleration SD, and maximum braking depth are at 40 to 70 km/h, 3 to 10 km/h, 0 to 0.6 m/s², and 14 to 16, respectively, there is a significant reduction in driving risk. The application of the IWS expands the effective range of mean speed and speed SD for reducing driving risk to 40 to 100 km/h and 3 to 15 km/h, respectively.