From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes

• URL: http://arxiv.org/abs/2605.06684v1
• Date: Sat, 25 Apr 2026 20:25:27 GMT
• Title: From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
• Abstract summary: Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions.<n>This study develops a comprehensive analytical framework to identify and quantify risk factors contributing to crash severity in tree-involved collisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impacts. This study develops a comprehensive analytical framework to identify and quantify risk factors contributing to crash severity in tree-involved collisions using the Crash Report Sampling System (CRSS) database spanning 2020-2023. The modeling framework follows a multi-step process. First, a machine learning based classification model (CatBoost) identifies key factors associated with binary crash injury severity (KA: fatal or incapacitating injury versus BC: non-incapacitating or possible injury). Second, SHapley Additive exPlanations (SHAP) tool is used to quantify and visualize the marginal effects of top influential factors on crash severity. Third, a binary logistic regression model estimates factor effects and validates SHAP-derived importance measures. Finally, SHAP interaction plots examine the combined effects of key contributing factors. Results reveal restraint non-use as the most influential predictor, with unrestrained occupants nearly three times more likely to experience severe outcomes due to ejection risk. Vehicle age, speeding violations, and driver impairment demonstrate substantial effects, reflecting reduced crashworthiness, increased impact forces, and reduced control capabilities. Critical interactions emerge between lighting conditions and vehicle age, speeding and lighting conditions, restraint use and vehicle age, and road surface and speeding, demonstrating additive risk effects with specific interactions. These findings provide critical insights for targeted safe system-based interventions, including enhanced seat belt enforcement, speed management in reduced visibility conditions, and vehicle fleet modernization.

Related papers

• Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation [74.17379276939599]
  We show that activation steering can induce broad misalignment, even in the recent Qwen-3.5 series.<n>We characterize properties of AS-induced EM by analyzing key steering-specific factors, including steering magnitude, the low-rank structure of the steering subspace, and the number of epochs during steering-vector construction.
  arXiv  Detail & Related papers  (2026-06-07T15:34:59Z)
• Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
  We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
  arXiv  Detail & Related papers  (2026-05-18T08:45:24Z)
• ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving [17.936492070548]
  Existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections.<n>This study introduces ROAR, a novel approach for accident detection and prediction.<n> ROAR combines Discrete Wavelet Transform (DWT), a self adaptive object aware module, and dynamic focal loss to tackle these challenges.
  arXiv  Detail & Related papers  (2025-11-09T04:55:37Z)
• Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection [1.0941365324532635]
  This study introduces a curated dataset of more than 3 million people involved in accidents in Ohio over six years-2022.<n>The primary contribution is a transparent and reproducible methodology that combines Automated Machine Learning (AutoML) and explainable artificial intelligence (AI) to identify and interpret key risk factors associated with severe crashes.<n>Key features spanned demographic, environmental, vehicle, human and operational categories, including location type posted speed, minimum occupant age, and pre-crash action.
  arXiv  Detail & Related papers  (2025-08-15T14:31:26Z)
• Towards Reliable and Interpretable Traffic Crash Pattern Prediction and Safety Interventions Using Customized Large Language Models [14.53510262691888]
  TrafficSafe is a framework that adapts to reframe crash prediction and feature attribution as text-level reasoning.<n>Alcohol-impaired driving is the leading factor in severe crashes.<n>TrafficSafe highlights pivotal features during model training guiding strategic crash data collection improvements.
  arXiv  Detail & Related papers  (2025-05-18T21:02:30Z)
• Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors [0.0]
  This research leverages large language model (LLM) to analyze freeway crash data and provide crash causation analysis accordingly.<n>The fine-tuned Llama3 8B model was then used to identify crash causation without pre-labeled data through zero-shot classification.<n>Results demonstrate that LLMs effectively identify primary crash causes such as alcohol-impaired driving, speeding, aggressive driving, and driver inattention.
  arXiv  Detail & Related papers  (2025-05-15T04:07:55Z)
• Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
  We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports. We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes. Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
  arXiv  Detail & Related papers  (2024-06-16T03:10:16Z)
• Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach [15.717402981513812]
  This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations.
  arXiv  Detail & Related papers  (2024-01-01T15:03:14Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [46.909086734963665]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.<n>Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.<n> RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding [50.81809690183755]
  Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. Previous research primarily assessed the importance of individual participants, treating them as independent entities. We introduce Driving scene Relationship self-Understanding transformer (DRUformer) to enhance the important object detection task.
  arXiv  Detail & Related papers  (2023-11-11T07:26:47Z)
• Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes [0.4129225533930965]
  This study tries to eliminate shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as a predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or
  arXiv  Detail & Related papers  (2023-01-04T13:07:28Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.