Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques

• URL: http://arxiv.org/abs/2509.07845v1
• Date: Tue, 09 Sep 2025 15:22:14 GMT
• Title: Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
• Abstract summary: This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity.<n>Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed to extract semantic meaning.<n>102 machine learning models were developed by combining structured features and narrative-based features.
• Score: 5.145333626258798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed to extract semantic meaning from the narratives, and their effectiveness was compared. To address the challenge of class imbalance, a K-Nearest Neighbors-based oversampling method was applied to the training data prior to modeling. The dataset consists of crash records from Kentucky spanning 2019 to 2023. To account for roadway heterogeneity, three road classification schemes were used: (1) eight detailed functional classes (e.g., Urban Two-Lane, Rural Interstate, Urban Multilane Divided), (2) four broader paired categories (e.g., Urban vs. Rural, Freeway vs. Non-Freeway), and (3) a unified dataset without classification. A total of 102 machine learning models were developed by combining structured features and narrative-based features using the two NLP techniques alongside three ensemble algorithms: XGBoost, Random Forest, and AdaBoost. Results demonstrate that models incorporating narrative data consistently outperform those relying solely on structured data. Among all combinations, TF-IDF coupled with XGBoost yielded the most accurate predictions in most subgroups. The findings highlight the power of integrating textual and structured crash information to enhance person-level injury prediction. This work offers a practical and adaptable framework for transportation safety professionals to improve crash severity modeling, guide policy decisions, and design more effective countermeasures.

Related papers

• 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)
• Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL) [0.0]
  This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narratives.<n>Model performance was systematically validated against expert reviews of potentially misclassified narratives.<n>We conclude that hybrid approaches combining automated classification with targeted expert review offer a practical methodology for improving crash data quality.
  arXiv  Detail & Related papers  (2025-07-03T17:26:27Z)
• Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods [51.28632782308621]
  We focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group.<n>We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM)<n>Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle.
  arXiv  Detail & Related papers  (2025-07-01T09:20:41Z)
• Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety [41.76653295869846]
  We analyze traffic incidents in Munich to identify patterns and characteristics that distinguish different types of accidents.<n>The dataset consists of both structured tabular features, such as location, time, and weather conditions, as well as unstructured free-text descriptions detailing the circumstances of each accident.<n>To assess the reliability of labels, we apply NLP methods, including topic modeling and few-shot learning, which reveal inconsistencies in the labeling process.
  arXiv  Detail & Related papers  (2025-06-11T14:50:49Z)
• CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis [0.46040036610482665]
  Road crashes claim over 1.3 million lives annually worldwide and incur global economic losses exceeding $1.8 trillion.<n>This study presents CrashSage, a novel Large Language Model (LLM)-centered framework designed to advance crash analysis and modeling through four key innovations.
  arXiv  Detail & Related papers  (2025-05-08T00:23:18Z)
• 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)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework [68.04940365847543]
  We propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data.
  arXiv  Detail & Related papers  (2022-10-30T10:15:21Z)
• Distant finetuning with discourse relations for stance classification [55.131676584455306]
  We propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater.
  arXiv  Detail & Related papers  (2022-04-27T04:24:35Z)
• 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.