Crash Severity Prediction Using Deep Learning Approaches: A Hybrid CNN-RNN Framework

• URL: http://arxiv.org/abs/2510.04316v1
• Date: Sun, 05 Oct 2025 18:31:45 GMT
• Title: Crash Severity Prediction Using Deep Learning Approaches: A Hybrid CNN-RNN Framework
• Authors: Sahar Koohfar,
• Abstract summary: The study was conducted using a dataset of 15,870 accident records gathered over a period of seven years between 2015 and 2021 on Virginia highway I-64.<n>The proposed CNN-RNN hybrid model has outperformed all benchmark models in terms of predicting crash severity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. In order to provide appropriate levels of medical assistance and transportation services, an intelligent transportation system relies on effective prediction methods. Deep learning models have gained popularity in this domain due to their capability to capture non-linear relationships among variables. In this research, we have implemented a hybrid CNN-RNN deep learning model for crash severity prediction and compared its performance against widely used statistical and machine learning models such as logistic regression, na\"ive bayes classifier, K-Nearest Neighbors (KNN), decision tree, and individual deep learning models: RNN and CNN. This study employs a methodology that considers the interconnected relationships between various features of traffic accidents. The study was conducted using a dataset of 15,870 accident records gathered over a period of seven years between 2015 and 2021 on Virginia highway I-64. The findings demonstrate that the proposed CNN-RNN hybrid model has outperformed all benchmark models in terms of predicting crash severity. This result illustrates the effectiveness of the hybrid model as it combines the advantages of both RNN and CNN models in order to achieve greater accuracy in the prediction process.

Related papers

• Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach [55.43914153271912]
  Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features.<n>Existing GNNs fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading to unreliable and risky predictions in real-world scenarios.<n>We propose a novel Evidence Fusing Graph Neural Network (EFGNN for short) to achieve trustworthy prediction, enhance node classification accuracy, and make explicit the risk of wrong predictions.
  arXiv  Detail & Related papers  (2025-06-16T03:59:38Z)
• Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by Hybrid VAE-Diffusion-Based Generative Neural Networks [25.772405506451204]
  A key challenge in crash frequency modelling is the prevalence of excessive zero observations.<n>We propose a hybrid VAE-Diffusion neural network, designed to reduce zero observations.<n>We assess the synthetic data quality generated by this model through metrics like similarity, accuracy, diversity, and structural consistency.
  arXiv  Detail & Related papers  (2025-01-17T07:53:27Z)
• Multi-class real-time crash risk forecasting using convolutional neural network: Istanbul case study [0.0]
  The performance of an artificial neural network (ANN) in forecasting crash risk is shown in this paper. The proposed CNN model is capable of learning from recorded, processed, and categorized input characteristics. The findings of this research suggest applying the CNN model as a multi-class prediction model for real-time crash risk prediction.
  arXiv  Detail & Related papers  (2024-02-09T10:51:09Z)
• 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)
• Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction [15.248412426672694]
  Graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature. In this work, we classify the components of successful GCNN prediction models and analyze the effects of factorization, attention mechanism, and weight sharing on their performance.
  arXiv  Detail & Related papers  (2023-05-30T00:50:51Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting [6.295157260756792]
  We introduce the Probabilistic AutoRegressive Neural Networks (PARNN) PARNN is capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. We evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR.
  arXiv  Detail & Related papers  (2022-04-01T17:57:36Z)
• Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models [4.353029347463806]
  We propose an adversarial attack framework by treating the prediction model as a black-box. The adversary can oracle the prediction model with any input and obtain corresponding output. To test the attack effectiveness, two state of the art, graph neural network-based models (GCGRNN and DCRNN) are examined.
  arXiv  Detail & Related papers  (2021-10-17T03:45:35Z)
• Neural Architecture Dilation for Adversarial Robustness [56.18555072877193]
  A shortcoming of convolutional neural networks is that they are vulnerable to adversarial attacks. This paper aims to improve the adversarial robustness of the backbone CNNs that have a satisfactory accuracy. Under a minimal computational overhead, a dilation architecture is expected to be friendly with the standard performance of the backbone CNN.
  arXiv  Detail & Related papers  (2021-08-16T03:58:00Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• 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)
• Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [121.10450359856242]
  Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Existing approaches for uncertainty quantification in RNNs are based predominantly on Bayesian methods. We develop a frequentist alternative that: (a) does not interfere with model training or compromise its accuracy, (b) applies to any RNN architecture, and (c) provides theoretical coverage guarantees on the estimated uncertainty intervals.
  arXiv  Detail & Related papers  (2020-06-20T22:45:32Z)

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.