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Trajectory data of road users play a crucial role in transportation engineering and traffic safety research. Previous vehicle trajectory data, including naturalistic driving data, trajectory data captured by drones and traffic cameras, have greatly promoted the research of traffic safety, traffic simulation, and other transportation engineering fields. However, existing technologies in collecting trajectory data are limited their detection range. With the development of the sensing and tracking technologies, it is possible to track the trajectory information of road users at the wide-area level, i.e., the real-time trajectory data of all road users on the entire road. Recently, we published our data sharing platform, Tongji Road Trajectory Sharing (TJRD TS, available online at https://tjrdts.com) Platform, providing our open wide-area vehicle trajectory data for researchers, academics, and road traffic professionals. Advanced vehicle tracking and trajectory splicing technologies, such as millimeter wave radar and edge computing technology, were employed to obtain such wide-area vehicle trajectory data. In this short communication article, we brainstorm ideas of using wide-area vehicle trajectory data in traffic safety research, traffic efficiency research, and connected and autonomous vehicle (CAV) application. We hope this short communication and our published data sharing platform can serve as the steppingstone for those who are interested in road transportation engineering and road safety research using such wide-area vehicle trajectory data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to ensure driving safety. However, most previous studies on LC risk prediction did not consider the driver's intention recognition, which made it difficult to guarantee the timeliness and practicability of LC risk prediction. Moreover, the difference in driving risks and its influencing factors between LC to left lane (LCL) and LC to right lane (LCR) have rarely been investigated. To bridge the above research gaps, this study proposes a proactive LC risk prediction framework which integrates the LC intention recognition module and LC risk prediction module. The Long Short-term Memory (LSTM) neural network with time-series input was employed to recognize the driver's LC intention. The Light Gradient Boosting Machine (LGBM) algorithm was then applied to predict the LC risk. Feature importance analysis was lastly conducted to obtain the key features that affect the LC risk. The highD trajectory dataset was used for framework validation. Results show that the recognition accuracy of the driver's LCL, LCR and lane-keeping (LK) intentions based on the proposed LSTM model are 97%, 96% and 97%, respectively. Meanwhile, the LGBM algorithm outperforms other machine learning algorithms in LC risk prediction. The results from feature importance analysis show that the interaction characteristics of the LC vehicle and its preceding vehicle in the current lane have the greatest impact on the LC risk. The proposed framework could potentially be implemented in advanced driver-assistance system (ADAS) or autonomous driving system for improved driving safety., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Real-time driving risk status prediction is critical for developing proactive traffic intervention strategies and enhance driving safety. However, the optimal observation time window length and prediction time window length, which should be the prerequisite for the timeliness and accuracy of real-time driving risk status prediction model, have been rarely explored in previous studies. In this study, a methodology which integrates driving risk status identification, rolling time window-based feature extraction, real-time driving risk status prediction and driving risk influencing factors analysis was proposed to accurately evaluate and predict real-time driving risk status. The methodology was tested based on 1,440 car-following events from Shanghai Naturalistic Driving Study. Results show that four driving risk statuses (safe, low-risk, median-risk and high-risk) are most appropriate to establish risk labelling criteria. In addition, results from driving risk status prediction show that when the observation time window length is 0.5 s, the accuracy rate of predicting medium-risk or high-risk status occurring in the next 0.7 s is higher than 85 % using multi-layer perceptron model. Meanwhile, the results from the analysis of influencing factors show that the input variables related to the risk status score higher in the ranking of feature importance. A part from that, speed difference, headway distance, speed and acceleration are still important in predicting driving risk status. The proposed methods in this paper can be applied in connected and autonomous vehicle (CAV) to reduce driver cognitive workload and hence improve driving safety fed with naturalistic driving data collected using in-vehicle systems., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Previous studies have focused on the impact of visibility level on drivers' behavior and their safety in foggy weather. However, other important environmental factors such as road alignment have not been considered. This paper aims to propose a methodology in investigating rear-end collision avoidance behavior under varied foggy conditions, with focusing on changes in visibility and road alignment in this study. A driving simulator experiment with a mixed 2 × 4 × 6 factor design was conducted using an advanced high-fidelity driving simulator. The design matrix includes two safety-critical conditions, four visibility conditions, and six road alignment situations (in terms of the road curve and slope). Behavior variables from different dimensions were identified and compared under varied conditions. To estimate the safety of drivers, a time-based measurement, speed reduction time, is selected among the variables as a measure of safety. The survival analysis approach was introduced to model the relationship between environmental factors and driver safety, using speed reduction time as the survival time. Both the Kaplan-Meier method and the COX model were applied and compared. Results generally suggest that reduced visibility leads to more dangerous rear-end collision avoidance behavior from different aspects. Though findings are mixed regarding the road alignment, the impact of the road alignment was found to be significant. Interestingly, conditions of downward slope were found to be safer. Overall, the COX model outperformed the Kaplan-Meier method in understanding the impact of environmental factors, and it can be applied to investigate other contributing factors for freeway safety under foggy weather conditions., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)