Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator T_i was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/T_i indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for T_i, which T_i indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved T_i can overcome these shortcomings; thus, T_i has the highest correlation. More data are needed to verify and support the study.

Driving style assessment plays an important role in intelligent transportation system (ITS) applications, such as driving feedback provision and usage-based insurance. Many previous studies used supervised algorithms to profile drivers. However, this cannot be applied to large-scale unlabeled driving data, which are increasingly prevalent in the ITS context. This paper proposes a framework that combines lateral and longitudinal accelerations to assess a driver’s driving style using an unsupervised approach. The framework first detects risky acceleration maneuvers using a statistical method based on the G-G diagram that shows combinations of lateral and longitudinal accelerations. Hierarchical clustering was used to classify the average driving behavior of drivers into high-, medium-, and low-risk groups. Further, a unique Gaussian mixture model is trained for each driver to score their driving style and decompose risky acceleration maneuvers into several risk components. Finally, the spatio-temporal characteristics are extracted to provide implicit factors on the risky behavior of drivers. The proposed method was applied to a large-scale dataset obtained by in-vehicle data recorders. The results demonstrate the necessity to combine the two axes of acceleration for driver behavior assessment. The proposed method can model individual driving styles effectively from the driver’s G-G diagram, enabling applicability to large-scale unlabeled data for driving style assessment. The extraction of spatio-temporal characteristics can improve the interpretability of the results obtained using machine learning algorithms. All these results can be used to create a driver’s risk profile and provide tailored feedback for improving driving safety.