Highway construction zones present substantial safety challenges due to their
dynamic and unpredictable traffic conditions. With the rising number of highway
projects, limited accident data during brief construction phases underscores the
need for alternative safety evaluation methods, such as traffic conflict
analysis. This study addresses vehicular safety issues within the Kunshan
section of the Shanghai-Nanjing Expressway, focusing on conflict risk assessment
through a spatio-temporal analysis of a construction zone. Using drone-captured
video, vehicle trajectories were extracted to derive key operational indicators,
including speed and acceleration, providing a spatio-temporal foundation for
analyzing traffic flow and conflict dynamics. A novel **Comprehensive Collision
Risk Index (CCRI)** was introduced, integrating Time-to-Distance-to-Collision
(TDTC) and Enhanced Time-to-Collision (ETTC) metrics to enable a
multidimensional assessment of conflict risk. The CCRI captures both
longitudinal and lateral risks across varied traffic scenarios, offering a
robust indicator of conflict distribution, severity, and spatial characteristics
within the zone. Conflicts with CCRI values exceeding 20 seconds are considered
non-critical, indicating minimal risk. To predict conflict severity, three
machine learning models—Logistic Regression, Random Forest, and Multi-Layer
Perceptron (MLP) Neural Network—were developed and compared. The Random Forest
and MLP models demonstrated superior predictive accuracy and stability, with MLP
achieving balanced performance across both severe and general conflict
categories. Additionally, spatio-temporal analysis of CCRI values identified
transition zones as lower-risk areas, with factors such as speed and distance
differentials emerging as primary contributors to conflict severity. This
research advances traffic safety evaluation in construction zones by introducing
CCRI as a comprehensive, spatio-temporal risk metric and leveraging machine
learning for precise conflict severity prediction. The findings provide valuable
insights for developing targeted safety interventions and adaptive traffic
management strategies, offering crucial support for transportation engineers,
safety practitioners, and policymakers in enhancing safety within dynamic,
high-risk construction environments.