Vehicle-group-based Crash Risk Formation and Propagation Analysis for
  Expressways
Tianheng Zhu Ling Wang Yiheng Feng Wanjing Ma Mohamed Abdel-Aty
Previous studies in predicting crash risk primarily associated the number or
likelihood of crashes on a road segment with traffic parameters or geometric
characteristics of the segment, usually neglecting the impact of vehicles'
continuous movement and interactions with nearby vehicles. Advancements in
communication technologies have empowered driving information collected from
surrounding vehicles, enabling the study of group-based crash risks. Based on
high-resolution vehicle trajectory data, this research focused on vehicle
groups as the subject of analysis and explored risk formation and propagation
mechanisms considering features of vehicle groups and road segments. Several
key factors contributing to crash risks were identified, including past
high-risk vehicle-group states, complex vehicle behaviors, high percentage of
large vehicles, frequent lane changes within a vehicle group, and specific road
geometries. A multinomial logistic regression model was developed to analyze
the spatial risk propagation patterns, which were classified based on the trend
of high-risk occurrences within vehicle groups. The results indicated that
extended periods of high-risk states, increase in vehicle-group size, and
frequent lane changes are associated with adverse risk propagation patterns.
Conversely, smoother traffic flow and high initial crash risk values are linked
to risk dissipation. Furthermore, the study conducted sensitivity analysis on
different types of classifiers, prediction time intervalsss and adaptive TTC
thresholds. The highest AUC value for vehicle-group risk prediction surpassed
0.93. The findings provide valuable insights to researchers and practitioners
in understanding and prediction of vehicle-group safety, ultimately improving
active traffic safety management and operations of Connected and Autonomous
Vehicles.