Deep Learning-Based Convolutional Neural Network for Crash Severity Prediction

Conference proceedings article


ผู้เขียน/บรรณาธิการ


กลุ่มสาขาการวิจัยเชิงกลยุทธ์


รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่งChamroeun Se, Thanapong Champahom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong, Tassana Boonyoo, Vatanavongs Ratanavaraha

ปีที่เผยแพร่ (ค.ศ.)2024

URLhttps://cita.vku.udn.vn/


บทคัดย่อ

Predicting traffic crash severity is vital for enabling data-driven policies and interventions to improve road safety. This study aims to evaluate customized Convolutional Neural Network (CNN) architectures for classifying single-motorcycle crash severity in Thailand using police reports from 2018-2020. Systematic experiments reveal substantial variability in model performance across different CNN layouts and layer depths. The peak-performing architecture proves to be a 4-layer dropout-regularized CNN which improves the F1-score by over 2 percentage points compared to a standard CNN baseline. Additionally, this optimized model achieves an aggregate trade-off score (between prediction accuracy rate and false positive rate) of 72.23%-over 5 points ahead of other variants (including the logistic regression and multilayer-perceptron neural network models). It demonstrates resilient precision and reliability in classifying both severe and fatal crashes, even with increasing depth. However, the dataset encompasses just over 2,900 motorcycle crash cases, constraining feasible model complexity. Significantly larger datasets could enable further performance gains from depth and regularization as shown through initial experiments. Overall, this study highlights the promise of applying customized deep learning techniques to unlock essential insights from traffic injury data for guiding impactful road safety policies.


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อัพเดทล่าสุด 2024-08-08 ถึง 00:00