Prediction and analysis of likelihood of freeway crash occurrence considering risky driving behavior.

The prediction of the likelihood of vehicle crashes constitutes an indispensable component of freeway safety management. Due to data collection limitations, studies have used mainly traffic flow-related variables to develop freeway crash prediction models but rarely have considered the effect of risky driving behavior on the likelihood of crashes. This study employed navigation software to collect driving behavior data and integrated multi-source data that include vehicle speed, traffic volume, and congestion index values. The study also employed the 'synthesizing minority oversampling technique and edited nearest neighbor' (SMOTE + ENN) coupled method for data balance processing. Three freeway crash likelihood prediction models were built based on the binomial logit, eXtreme Gradient Boosting (XGBoost), and support vector machine algorithms, respectively. The Shapley additive explanation (SHAP) algorithm was utilized to explore the effect of each feature variable on the likelihood of crashes. The results show that the prediction accuracy of the XGBoost model is the best of the three compared models. Under the optimal control-to-case ratio (1:1), the prediction accuracy of the XGBoost model reached 0.96 in this study, and the recall rate, specificity, and area-under-the-curve values were 0.86, 0.96, and 0.907, respectively. Comparative test results demonstrate that ranking risky driving behavior into three levels of intensity can effectively enhance the predictive accuracy of the XGBoost model. Moreover, the XGBoost model with its ten-minute time step outperformed the XGBoost model with its five-minute time step in terms of prediction accuracy. The results of the SHAP-based analysis show that the likelihood of highway crashes is high when the traffic congestion level is high and the distribution of the vehicle speed in the upstream roadway section is significant. Also, both sharp acceleration and sharp deceleration lead to greater likelihood of crashes. This paper aims to provide an effective framework for predicting and interpreting the likelihood of freeway crashes, thereby providing guidance for crash prevention, driver training, and the development of traffic regulations.

A dynamic method to predict driving risk on sharp curves using multi-source data.

Traffic accidents are likely to occur on sharp curves under poor driving conditions, and the severity level of such accidents is high. Therefore, predicting the risk associated with driving on curved roadways in real time can effectively improve driving safety. This paper aims to develop a dynamic real-time method that fuses multiple data sources to predict risk when driving on sharp curves in the context of the connected vehicle environment. Six curves with three small radii (60 m, 100 m, 150 m) and two driving directions (left and right) were designed for a driving simulation experiment. Driver maneuvering data, vehicle kinematic data, and physiological data of 55 drivers were collected for this study. The data were combined and spatially and dynamically segmented. The mean value of the critical lateral acceleration of the vehicle was set as the risk assessment index. K-means clustering was used to classify the driving risk into three levels: low, medium, and high. Then, the risk level was predicted using the maneuvering data, vehicle kinematic data, and physiological data as well as road alignment characteristics as input features for the proposed model that employs the long short-term memory (LSTM) network algorithm. Models with different combinations of observation window (lookback) and interval window (delay) were compared to derive the best window combination. The algorithms selected for comparison against the LSTM algorithm are random forest, XGBoost, and LightGBM. The results show that the proposed LSTM-based method can effectively predict dangerous driving behavior on sharp curves. The optimal window combination derived using the LSTM algorithm is lookback = 20 m and delay = 20 m. The prediction performance of the proposed model is significantly better than that of the other three compared algorithms, with F1-scores of 84.8% and 86.0% for the medium and high risk categories, respectively. In addition, the proposed LSTM-based model that fuses multiple data sources is proven to outperform the model that uses only vehicle kinematics data. The dynamic prediction method proposed in this paper can contribute to the development of a real-time prediction and warning system for driving risks at vehicle terminals in the intelligent connected vehicle environment.

Impact of the connected vehicle environment on tunnel entrance zone.

The drastic changes of the space environment at the tunnel entrance can lead to frequent accidents with higher levels. The connected vehicle environment provides drivers with surrounding traffic information and improve their driving behavior by helping them make safe decisions efficiently. As such, this study is to examine the effects of the connected vehicle environment on driving behavior and safety at the tunnel entrance zone. To this end, this research simulates a connected vehicle environment and provides driving aids through the Human-Machine Interface (HMI). Secondly, 40 participants with diverse backgrounds drove the simulator under two different driving conditions: HMI-OFF (traditional driving environment) and HMI-ON (connected vehicle environment). Finally, indicators are selected from speed control, stability and urgency to analyze the impact of the connected vehicle environment on drivers' behaviors and safety at the warning zone and tunnel entrance zone. The results show that in the connected vehicle environment, the drivers' speed control in the warning zone is improved and their deceleration behavior is advanced. The driver's speed control and stability are improved while the danger level of the accident is reduced 100 m ahead of the tunnel entrance. Besides, the driver's speed control and stability have been both improved 300 m after the tunnel entrance. Overall, in the connected vehicle environment, the driver can recognize the tunnel in advance and adjust his driving speed in time to ensure his safety at the tunnel entrance. The results of this study play a critical role in the design and research of warning systems in a connected vehicle environment, and will also guide vehicle manufacturers in designing safety-related functions of automated vehicles. In this research, a connected vehicle environment test platform based on driving simulation technology is constructed and tested in specific tunnel entrance scenarios, which provides a reference for realizing active protection of vehicles at the tunnel entrance.