Influence of Landscape Design on Driving Behavior Based on Slope Illusion

In a continuous downhill section of a mountain highway, factors such as road alignment, roadside environment, and other visual characteristics will impact the slope illusion drivers experience and engage in unsafe driving behaviors. To improve the negative consequences of slope illusion and driving safety in continuous downhill sections, the effects of plant spacing, height, roadside distance, and color on driving behavior were all studied by simulating the plant landscape in a virtual environment. A driving simulator and UC-win/road software were used to conduct an indoor driving simulation experiment, and parameters such as speed and lateral position offset were used as the evaluation indices of driving stability to reflect the driver’s speed perception ability with subjective equivalent speeds. The results show that a plant landscape with appropriate plant spacing, height, roadside separation, and color is conducive to improving driving stability. Furthermore, a landscape with a height of 3 m, spacing of 10 m, roadside spacing of 0.75 m, and appropriate color matching can enhance the slope perception ability and speed perception ability of drivers, which is conducive to improving the driving safety of continuous downhill sections.

Understanding Traffic Accidents among Young Drivers in Qatar

Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers’ demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.

Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM

Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by preprocessing the data. A recognition model based on a convolutional neural network combined with a long short-term memory network was constructed to extract depth features from data sets and recognize abnormal driving behaviors. The test results show that the accuracy of the model based on CNN-LSTM can reach 95.22%, and the performance indexes can reach more than 94%. Compared with the recognition model constructed only by CNN or LSTM, this model has higher recognition accuracy.

Operating Safety Evaluation of Battery-Electric Taxi Based on Spatio-Temporal Speed Parameters

The battery-electric taxis have the features of larger mass, low operating noise, and great speed, and the drivers of battery-electric taxis have various driving behaviors and low safety awareness, which leads to higher safety risks. In the paper, the driving and speed characteristics of battery-electric taxis, conventional taxis, and private cars are compared and analyzed through conducting a GPS trajectory survey and a cross-section traffic flow parameter survey. An evaluation index system that is based on the spatio-temporal speed parameters is proposed, and a MEW-VIKOR method is developed for the operatiing safety evaluation of the battery-electric taxi. The results show that the operating speed of battery-electric taxis is significantly higher than that of conventional taxis on weekdays and weekends, and there is a relatively common speeding phenomenon on urban local roads. The proposed safety evaluation index system that is based on the spatio-temporal speed parameters and the MEW-VIKOR evaluation method can effectively evaluate the operatiing safety of battery-electric taxis. In addition, the ranking results show that, according to the spatio-temporal speed parameters, the operating safety of battery-electric taxis is lower than that of conventional taxis and private cars. The research provides theoretical insights for strategies and policies making to reduce the unsafe driving behaviors of battery-electric taxis.

Driving Habits of Older Adults in Massachusetts: Variations by Sex, Age, Race, Income, Housing Density, and Health

Maintaining ability to drive is critical to independent living among older adults residing in suburban and rural communities. We administrated structured questionnaire about driving behaviors to 370 persons age 65 and older living in Central Massachusetts between 2018 and 2020. Of them, 307 were active drivers. Driving in the past year was strongly associated with being male, White race, higher income, non-urban resident, and good-to-excellent health. Advancing age was associated with lower frequency of driving, less miles driven, lower percentage of the day spent in transportation. Men and women drove with nearly equal frequency (~26 days/month), but men drove significantly more miles. Non-White drivers were significantly more likely to avoid driving out of town or in difficult conditions, even after controlling for age, sex, income, and density of residential area. In conclusion, driving behaviors differed significantly by age, sex, income, race, and housing density. Further investigation is warranted.