Drivers gather traffic information primarily by means of their vision. Especially during complicated maneuvers, such as overtaking, they need to perceive a variety of characteristics including the lateral and longitudinal distances with other vehicles, the speed of others vehicles, lane occupancy, and so on, to avoid crashes. The primary object of this study is to examine the appropriate visual search patterns during overtaking maneuvers on freeways. We designed a series of driving simulating experiments in which the type and speed of the leading vehicle were considered as two influential factors. One hundred and forty participants took part in the study. The participants overtook the leading vehicles just like they would usually do so, and their eye movements were collected by use of the Eye Tracker. The results show that participants’ gaze durations and saccade durations followed normal distribution patterns and that saccade angles followed a log-normal distribution pattern. It was observed that the type of leading vehicle significantly impacted the drivers’ gaze duration and gaze frequency. As the speed of a leading vehicle increased, subjects’ saccade durations became longer and saccade angles became larger. In addition, the initial and destination lanes were found to be key areas with the highest visual allocating proportion, accounting for more than 65% of total visual allocation. Subjects tended to more frequently shift their viewpoints between the initial lane and destination lane in order to search for crucial traffic information. However, they seldom directly shifted their viewpoints between the two wing mirrors.

The prediction and identification of key factors in road traffic accidents are crucial for accident prevention, yet previous studies have often examined these aspects separately. To comprehensively assess the risk level of road traffic accidents and their key determinants, this paper proposes a comprehensive forecasting and analysis framework that offers a novel perspective for identifying key risk factors from a modeling standpoint compared to existing methods. The CNN-BiLSTM-Attention model was developed for predicting the risk value of road accidents, and DeepSHAP was employed to interpret the model and extract the key factors contributing to traffic accidents. This deep learning framework combines convolutional neural networks (CNN) and Bi-directional long short-term memory (BiLSTM), while incorporating a spatial-temporal local attention mechanism to enhance its capability in capturing spatiotemporal features. Through analysis and experimentation on real-world datasets, our model demonstrates superior accuracy in predicting traffic accident risk compared to the benchmark model, achieving a Mean Absolute Error (MAE) of 0.2475 on the UK dataset and 0.2683 on the US dataset. The results obtained from DeepSHAP were found to be more rational and informative in identifying key factors of different severity levels using four methods. To verify the rationality and stability of obtaining these key factors, the first 15 factors were reintegrated into the prediction model, resulting in almost unchanged accuracy and reduced model iteration time. By improving the influential factors, road traffic accidents can be effectively mitigated.

In China, the maximum design speed of highways is 120 km/h, which first appeared in the Highway Engineering Technical Standard (Trial) in 1951. However, vehicle performance, road design, and construction technology have been greatly improved over the past 68 years. To adapt to the development demands of highway design speeds above 120 km/h in the future, it is urgent to study superhighway alignment design theory. Therefore, the horizontal alignment security design theory of superhighways was developed in this paper. First, the definition, classification, and construction mode of a superhighway and suitable vehicles of different grades are presented. Then, the lengths of straight lines were limited to reduce driving fatigue. Next, the minimum radii of circular curves were calculated based on driver characteristics and stress analysis of operating vehicles. Finally, the minimum lengths of transition curves were calculated based on the centrifugal acceleration of the operating vehicles, the travel time, and the passenger visual characteristics. The calculation and analysis results show that the superhighway linear features conform to the vehicle operating characteristics, and can ensure the safety of driving.

To improve driving safety on superhighways, longitudinal profile design parameters of a superhighway are calculated via force analysis while a car is driven on a slope. The calculations consider characteristics of drivers, cars, and roads. According to the vehicle type, design speed, and natural conditions, the maximum longitudinal slope of a superhighway is calculated and compared with those of an ordinary superhighway and high-speed railway. Based on analysis of the vehicle climbing performance, braking performance, and driver visual characteristics, the maximum and minimum slope lengths of a superhighway are calculated. By analyzing the elements of vertical curves, the minimum radius and minimum length of the vertical curves of a superhighway are calculated by considering factors such as mitigating the impact at the slope bottom, driving at night, and driving time along vertical curves. Analysis and calculation results show that when the maximum longitudinal slope is 2.50%, 2.25%, and 2.00%, the minimum slope length is 450 m, 400 m, and 350 m, respectively, and the minimum vertical curve length is 145 m, 130 m, and 115 m, respectively, and the superhighway travel requirements can be satisfied at speeds of 180 km/h, 160 km/h, and 140 km/h, respectively.

To find and solve the problems existing in the development of higher education in China, the input–output, scale of higher education, students’ tuition and teachers’ income of Chinese and American universities are compared. The results show that the investment in top universities in China is similar to that in the United States, but the average student budget is much less, and the output is not comparable to that of American universities. The scale of higher education is much larger than that of the United States, and the growth rate is far more than demand. College tuition should be increased, with the absolute tuition only 5.93% of income, and relative tuition is 20.21% of that in the United States. College teachers are underpaid, earning only approximately 20% of what their peers earn in the United States. Therefore, for higher education sustainability, the paper puts forward the development direction of higher education in China, which is to control the expansion scale of colleges and universities, and to increase students’ tuition and teachers’ salary.

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.