Amirhossein Taheri | Searcharxiv

arXiv2026

Capturing Road-Level Heterogeneity in Crash Severity on Rural Two-Lane Highways: A Multilevel Statistical and Machine-Learning Analysis

Crash severity may vary across roads because crashes on the same road share contextual characteristics not fully represented by observed variables. This study examined road-level heterogeneity using 19,956 police-reported crashes from 100 rural two-lane highways in Iran during 2021-2024. Single-level logistic regression was compared with random-intercept and random-coefficient multilevel logistic models. A regression-based mixed-effects random forest (MERF) was also used for an exploratory assessment of nonlinear predictive relationships and road-level patterns. The null multilevel model produced an intraclass correlation coefficient of 20.3 percent, indicating meaningful latent between-road variation in crash severity. Pavement condition had the largest estimated random-slope variance, followed by lighting condition, driver education, and driver age. Logistic road-level random intercepts ranged from -2.87 to +1.51 on the log-odds scale. In a comparison excluding road-specific information, the ten-variable MERF random-forest component achieved an AUC of 0.701, compared with 0.638 for single-level logistic regression. Retaining MERF road-level corrections for roads represented during training increased AUC to 0.763. MERF road-level corrections were strongly associated with logistic random intercepts (Pearson r = 0.892; Spearman rho = 0.931), indicating similar relative road patterns, although the estimates are not numerically equivalent. The findings support complementary use of multilevel and nonlinear models. Road-level estimates may help prioritize further investigation, but they do not measure crash frequency, total road risk, or causal effects.

stat.AP↗

arXiv2025

UAV-Based Intelligent Traffic Surveillance System: Real-Time Vehicle Detection, Classification, Tracking, and Behavioral Analysis

Traffic congestion and violations pose significant challenges for urban mobility and road safety. Traditional traffic monitoring systems, such as fixed cameras and sensor-based methods, are often constrained by limited coverage, low adaptability, and poor scalability. To address these challenges, this paper introduces an advanced unmanned aerial vehicle (UAV)-based traffic surveillance system capable of accurate vehicle detection, classification, tracking, and behavioral analysis in real-world, unconstrained urban environments. The system leverages multi-scale and multi-angle template matching, Kalman filtering, and homography-based calibration to process aerial video data collected from altitudes of approximately 200 meters. A case study in urban area demonstrates robust performance, achieving a detection precision of 91.8%, an F1-score of 90.5%, and tracking metrics (MOTA/MOTP) of 92.1% and 93.7%, respectively. Beyond precise detection, the system classifies five vehicle types and automatically detects critical traffic violations, including unsafe lane changes, illegal double parking, and crosswalk obstructions, through the fusion of geofencing, motion filtering, and trajectory deviation analysis. The integrated analytics module supports origin-destination tracking, vehicle count visualization, inter-class correlation analysis, and heatmap-based congestion modeling. Additionally, the system enables entry-exit trajectory profiling, vehicle density estimation across road segments, and movement direction logging, supporting comprehensive multi-scale urban mobility analytics. Experimental results confirms the system's scalability, accuracy, and practical relevance, highlighting its potential as an enforcement-aware, infrastructure-independent traffic monitoring solution for next-generation smart cities.

cs.CV↗