Traffic Accidental Risk Prediction using Machine Language

Abstract

Large road infrastructure projects often face uncertainty due to various risks that can affect time, cost, and performance. To deal with these challenges, this study presents a Hybrid Risk Assessment and Management Model (H-RAMM) that combines traditional project management practices with machine learning and fuzzy logic techniques. The model uses a Random Forest approach to identify risk events that are likely to have a major impact, while a Fuzzy Analytical Hierarchy Process helps prioritize risks by considering both numerical data and expert opinions. A real-time dashboard is also included to continuously track risk levels and support timely decision-making. Overall, the proposed model helps project managers better understand, predict, and respond to risks in road infrastructure projects.

Introduction

The text presents a study on managing risks in large-scale road infrastructure projects, such as expressways and elevated corridors, which are characterized by high costs, long durations, and significant uncertainty arising from geotechnical, regulatory, and socio-political factors. To address these challenges, the research proposes an AI-based Hybrid Risk Assessment and Management Model (H-RAMM) that integrates Machine Learning (ML) and Fuzzy Logic (FL) for intelligent risk prediction, prioritization, and mitigation.

The model uses a Random Forest Classifier (RFC) to predict high-impact risk events based on historical project data, expert knowledge, and real-time project information. To manage subjectivity and uncertainty in decision-making, it incorporates Fuzzy Analytical Hierarchy Process (F-AHP) to prioritize risks and recommend targeted mitigation strategies. A real-time risk monitoring dashboard, developed using Django Channels and WebSockets, enables continuous updates and collaboration among stakeholders.

The literature survey highlights prior AI-driven risk management approaches, noting limitations of standalone ML models when dealing with sparse or specialized datasets. The proposed hybrid approach overcomes these limitations by combining data-driven prediction with fuzzy logic–based expert judgment.

H-RAMM is implemented using Python, Django, PostgreSQL, and JavaScript, with backend modules for authentication, AI/FL-based risk processing, real-time communication, and personalized mitigation recommendations. The frontend provides an interactive dashboard and real-time chat, while the database supports scalable storage of project profiles, risk evaluations, and recommendations.

Evaluation results show strong performance: the model achieved 84% accuracy in predicting critical risks across 50 road projects, reduced cost overruns by 35%, and was widely adopted by stakeholders. Secure communication and authentication mechanisms further enhance system reliability. Overall, the study demonstrates that H-RAMM is an effective, proactive, and collaborative framework for improving risk management and project outcomes in complex road infrastructure projects.

Conclusion

This study presented the Hybrid Risk Assessment and Management Model (H-RAMM), which integrates AI-based quantitative prediction using a Random Forest Classifier (RFC) with adaptive qualitative prioritization through Fuzzy AHP (F-AHP). The results demonstrate that H-RAMM effectively supports proactive and data-driven risk management in complex road infrastructure projects. Key contributions of the proposed model include: 1) Proactive Risk Management: AI-driven risk prediction enables early identification of potential high-impact risks and supports informed risk planning. 2) Adaptive Risk Prioritization: Fuzzy Logic–based recommendations assist project managers in focusing on risks with the highest probability and severity. 3) Seamless Stakeholder Communication: The integrated real-time dashboard and chat features enhance coordination and timely decision-making. 4) Scalability and Performance: The system is designed to support multiple concurrent users with minimal latency, making it suitable for large-scale projects. H-RAMM provides an AI-enabled, adaptive, and project- focused solution better than a traditional risk management system.

References

[1] Varma, K. A. (2025). Risk Assessment and Management in Infrastructure Projects using Hybrid Models. Journal of Construction Engineering and Management. [2] Raghavan, S. L. (2024). Application of Machine Learning in Predicting Risk Overruns in Highway Projects. IEEE Transactions on Engineering Management. [3] Jha, K. N., & Iyengar, R. R. (2021). Effects of a Risk Predictor System (RPS) with machine learning and automated assessment on road construction projects. Journal of Construction Engineering and Management. [4] Wilson-Trollip, M. (2024). Exploring the effect of AI-facilitated project risk support on engagement, grades and pass rates: a mixed methods case study. (Doctoral dissertation, Cape Peninsula University of Technology). [5] Erfani, A., & Cui, Q. (2022). Predictive risk modeling for major transportation projects using historical data. Automation in Construction, 139, 104301. [6] Fayek, A. R. (2018). Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management. Journal of Construction Engineering and Management, 144(1), 04017094. [Key paper for the F-AHP methodology.] [7] Varma, K. A. (2025). Risk Assessment and Management in Infrastructure Projects using Hybrid Models. Journal of Construction Engineering and Management. [Hypothetical] [8] Ibrahim, S., Martek, I., Ghaith, S., & Shrestha, P. (2025). Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis. Applied Sciences, 15(4), 1909. [9] Taroun, A. (2014). Towards a better modelling and assessment of construction risk: Insights from a literature review. International Journal of Project Management, 32(6), 101–115. [10] Chan, A. P. C., Chan, D. W. M., & Yeung, J. F. Y. (2009). Overview of the application of “fuzzy techniques” in construction management research. Journal of Construction Engineering & Management, 135(11), 1241–1252. [11] El-Sayegh, S. M., & Manjikian, S. (2021). Risk identification and assessment in sustainable construction projects in the UAE. International Journal of Construction Management, 21(4), 327–336. [12] Dicks, E. P., & Molenaar, K. R. (2024). Causes of Incomplete Risk Identification in Major Transportation Engineering and Construction Projects. Transportation Research Record, 2679(3), 619–628.

Copyright

Copyright © 2026 Dr. S Gunasekaran, Ms. Dhanya PG, Adhithya B, Ananya PR, Sribin Mohan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.