Journal of Transportation Systems

Predicting Serious Injury in ADAS-Involved Crashes: A Comparative Machine Learning Analysis of NHTSA Standing General Order Reports, 2021-2025

Antora Roy — 2026-09-07

Advanced Driver Assistance Systems (ADAS) are now standard equipment on most new vehicles, yet what happens to occupants once a crash does occur remains thinly documented outside simulation studies. This study models injury severity using crash reports filed under the National Highway Traffic Safety Administration (NHTSA) Standing General Order, covering January 2021 through June 2025. Records with unknown outcomes were dropped and duplicate filings consolidated, leaving 496 crashes. Severity was treated as binary: serious or fatal injury versus everything else. Twenty raw variables spanning environment, collision configuration, restraint deployment, vehicle attributes, and pre-crash motion were expanded to 80 encoded features, and five classifiers were compared under identical splits. Random Forest performed best, reaching 86.0% accuracy with an AUC-ROC of 0.859; five-fold cross-validation returned 84.6% ± 3.3%. Recall on the serious class was only 45.0%, however, meaning the model missed more than half of the severe outcomes it was built to catch. Manufacturer identity (Tesla, 15.26%) and model year (7.86%) outranked airbag status (11.04%) and collision configuration (8.86%) in the importance ranking. For screening use, the decision threshold must be lowered well below 0.5, trading precision for the sensitivity such applications require.

Road Safety: Driving Habits and Defensive Driving-A Comprehensive Analysis

Sadagoban Kalidass — 2026-09-08

Road traffic fatalities remain a critical socio-economic crisis, driven by individual behavioral failures and inadequate driver perception rather than a lack of statutory regulations. Conventional enforcement models intervene post-licensing, failing to restructure embedded, subconscious reflexes formed during early development. This paper establishes a holistic road safety model integrating human ethics, spatial mechanics, and institutional curriculum reform. "Observe, Monitor, Predict" (OMP) cognitive model for defensive driving, which trains drivers to continuously evaluate environmental variables, eliminate blind-spot risks, and project a 100-foot visual target path to mitigate panic braking. Furthermore, the proposal demonstrates how personal ethical traits, such as capacity awareness, situational yielding, and payload responsibility, directly mirror vehicle control mechanics. To turn these principles into lifelong reflexes, this policy proposal advocates for mandatory bicycle-based traffic education across Grades 1 to 8, utilizing "Children’s Traffic Parks" supervised by certified Physical Education Teachers. By aligning school-level practical training with statutory licensing standards (8th-standard qualification at age 18), this strategy transforms road safety from a reactive legal mandate into an internalized cultural responsibility, safeguarding human lives and national productivity.