Related Experiment Video
Updated: Jul 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
A novel cluster-based multinomial logit modeling for crash severity analysis in collisions involving automated
Tausif Islam Chowdhury1, Swastika Barua2, Sharif Ahmed Rafat2
1Civil Engineering Program, Texas State University, San Marcos, TX-78666, USA. sgp98@txstate.edu.
Abstract:
The increasing presence of Automated Electric Vehicles-Only Manufacturer (AEVOM) vehicles underscores the need for better understanding of crash severity under partial automation. Utilizing police-reported crash data from Texas between 2017 and 2024, this study applies a two-stage analytical framework to capture heterogeneity in crash outcomes. Variable selection and clustering validity are supported by Extreme Gradient Boosting (XGBoost) feature importance metrics and Cramér's V statistic. Cluster Correspondence Analysis (CCA) is used to classify crashes into four distinct typologies: high-speed highway crashes, intersection-related crashes, low-speed highway crashes with fixed objects, and crashes with parked-vehicles on non-trafficway locations.Within each cluster, Random Parameter Logit (RPL) and RPL with Heterogeneity in Means (RPLHM) models are estimated to account for unobserved heterogeneity in crash severity determinants. The analysis reveals that key variables such as lighting conditions, road classification, driver age, seatbelt use, and vehicle type influence severity outcomes differently across clusters. Notably, severe injuries are observed even in low-speed or seemingly controlled environments, highlighting functional limitations in current AEVOM vehicles' automation systems. This framework improves model fit and interpretability relative to aggregate models and provides insights to advanced driver-assistance systems, infrastructure design, and policy strategies aimed at enhancing the safety of semi-AEVs.
Related Concept Videos
Hypothesis Test for Test of Independence
H0: The two variables (factors)...
Elastic Collisions: Case Study
Determination of Expected Frequency
Collisions in Multiple Dimensions: Problem Solving
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Introduction to Test of Independence
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Collisions in Multiple Dimensions: Introduction