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2025 | OriginalPaper | Chapter

Enhanced CNN-Based Model for Traffic Risk Assessment

Authors : Soukaina Bouhsissin, Ayoub Jannani, Nawal Sael, Faouzia Benabbou

Published in: Innovations in Smart Cities Applications Volume 8

Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the critical area of traffic risk assessment, a pivotal component of intelligent transportation systems (ITS) aimed at enhancing road safety. The study introduces a novel convolutional neural network (CNN) model designed to classify traffic risks using a extensive dataset of dashcam images. The model's architecture, comprising multiple convolutional, pooling, and fully connected layers, is meticulously detailed, highlighting the design choices that enable effective feature extraction and classification. The chapter presents a rigorous methodology, including data preparation, preprocessing, and evaluation metrics, ensuring the model's reliability and accuracy. The proposed model achieves remarkable performance, with an accuracy of 99%, a loss function value of 0.03, and a ROC AUC score of 99.25%, significantly outperforming existing state-of-the-art models. The results demonstrate the model's potential for real-world applications in improving road safety and informing ITS. Additionally, the chapter discusses the challenges of traffic risk assessment and suggests future research directions, such as developing systems to detect and classify various traffic elements on the road. The comprehensive evaluation and comparison with other methods underscore the model's robustness and accuracy, making this chapter an essential read for those interested in advancing traffic risk assessment technologies.

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Literature
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Metadata
Title
Enhanced CNN-Based Model for Traffic Risk Assessment
Authors
Soukaina Bouhsissin
Ayoub Jannani
Nawal Sael
Faouzia Benabbou
Copyright Year
2025
DOI
https://doi.org/10.1007/978-3-031-88653-9_42