Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features

• URL: http://arxiv.org/abs/2602.16739v1
• Date: Tue, 17 Feb 2026 22:49:33 GMT
• Title: Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features
• Authors: Lei Han, Mohamed Abdel-Aty, Zubayer Islam, Chenzhu Wang,
• Abstract summary: We propose a hybrid crash likelihood prediction framework that does not depend on postcrash features.<n>A dynamic post-temporal window is designed to extract real-time traffic flow and environmental features from primary crash locations and their upstream segments.<n>Experiments on Florida freeways demonstrate that proposed the hybrid framework correctly identifies 91% of secondary crashes with a low false alarm rate of 0.20.
• Score: 6.477496237661746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Secondary crash likelihood prediction is a critical component of an active traffic management system to mitigate congestion and adverse impacts caused by secondary crashes. However, existing approaches mainly rely on post-crash features (e.g., crash type and severity) that are rarely available in real time, limiting their practical applicability. To address this limitation, we propose a hybrid secondary crash likelihood prediction framework that does not depend on post-crash features. A dynamic spatiotemporal window is designed to extract real-time traffic flow and environmental features from primary crash locations and their upstream segments. The framework includes three models: a primary crash model to estimate the likelihood of secondary crash occurrence, and two secondary crash models to evaluate traffic conditions at crash and upstream segments under different comparative scenarios. An ensemble learning strategy integrating six machine learning algorithms is developed to enhance predictive performance, and a voting-based mechanism combines the outputs of the three models. Experiments on Florida freeways demonstrate that the proposed hybrid framework correctly identifies 91% of secondary crashes with a low false alarm rate of 0.20. The Area Under the ROC Curve improves from 0.654, 0.744, and 0.902 for the individual models to 0.952 for the hybrid model, outperforming previous studies.

Related papers

• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• From Narratives to Probabilistic Reasoning: Predicting and Interpreting Drivers' Hazardous Actions in Crashes Using Large Language Model [3.3457493284891338]
  Two-vehicle crashes account for approximately 70% of roadway crashes.<n>Driver Hazardous Action (DHA) data is limited by inconsistent and labor-intensive manual coding practices.<n>Here, we present an innovative framework that leverages a fine-tuned large language model to automatically infer DHAs from textual crash narratives.
  arXiv  Detail & Related papers  (2025-10-14T21:35:47Z)
• 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)
• NsBM-GAT: A Non-stationary Block Maximum and Graph Attention Framework for General Traffic Crash Risk Prediction [11.444259609536164]
  Existing crash risk prediction models rely on hypothetical scenarios deemed dangerous by researchers.<n>Dashcam videos capture the pre-crash behavior of individual vehicles, but they often lack critical information about the movements of surrounding vehicles.<n>We propose a novel non-stationary extreme value theory (EVT) to capture the interactive behavior between a vehicle and its surrounding vehicles.
  arXiv  Detail & Related papers  (2025-03-06T02:12:40Z)
• Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic and Static Data with Generative Adversarial Networks [6.571659350175123]
  Secondary crashes significantly exacerbate traffic congestion and increase the severity of incidents.<n>Existing methods fail to fully address the complexity of traffic crash data, particularly the coexistence of dynamic and static features.<n>This study proposes a hybrid model named VarFusiGAN-Transformer, aimed at improving the fidelity of secondary crash data generation.
  arXiv  Detail & Related papers  (2025-01-17T08:56:49Z)
• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles.<n>Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements.<n>This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways [18.18842948832662]
  This paper introduces a prediction-based collision risk assessment approach on highways. We develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.
  arXiv  Detail & Related papers  (2022-05-03T07:58:02Z)
• 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)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

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.