PREDECTIVE ANALYSIS OF ROAD ACCIDENTS USING MACHING LEARNING (CNN)

Mr. Mali Amol K.; Prof.V.M.Chandode; Prof. S.V.Kulkarni

PREDECTIVE ANALYSIS OF ROAD ACCIDENTS USING MACHING LEARNING (CNN)

Authors: Mr. Mali Amol K., Prof.V.M.Chandode, Prof. S.V.Kulkarni

Unique Paper ID: 167550
Volume: 11
Issue: 3
PageNo: 1733-1739

Keywords: Predictive analysis road accidents machine learning Convolutional Neural Networks CNN traffic safety accident prevention data analytics predictive modeling

Abstract:
The increasing frequency of road accidents presents a significant challenge to public safety and urban planning. Leveraging advancements in machine learning, particularly Convolutional Neural Networks (CNNs), offers a promising approach to predictive analysis and accident prevention. This review paper provides a comprehensive analysis of recent developments in the application of CNN-based models for predicting road accidents. The study explores various data sources, including real-time traffic data, weather conditions, and road infrastructure, and examines the effectiveness of different CNN architectures in predicting accident likelihood and severity. Through a comparative analysis of existing models, the review identifies key factors influencing prediction accuracy and highlights the potential of CNNs in enhancing road safety. The findings suggest that CNNs, when integrated with advanced data analytics, can significantly improve the precision of road accident predictions, thereby aiding in the development of proactive measures to mitigate risks

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 3
PageNo: 1733-1739

PREDECTIVE ANALYSIS OF ROAD ACCIDENTS USING MACHING LEARNING (CNN)

Available:https://ijirt.org/Article?manuscript=167550

03 Dec, 2024

Traffic accident’s severity prediction: a deep learning approach-based CNN network: A Review

25 Aug, 2024

AFFORDABLE POTHOLE DETECTION AND REPORTING SYSTEM FOR LOCAL ROAD SAFETY USING MACHINE LEARNING AND ANDROID INTEGRATION

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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