Smart City Gnosys

Smart city article details

Title	Machine Learning Models For Road Accident Prediction For Smart Cities: A Comprehensive Analysis
ID_Doc	36014
Authors	Thanikachalam R.; Babu M.; Rahuman D.A.S.; Swain S.; Chandrasekaran S.; Veeran R.
Year	2025
Published	International Journal of Basic and Applied Sciences, 14, 2
DOI	http://dx.doi.org/10.14419/gw398342
Abstract	Road accidents are still a prominent urban issue, worsened by a growing population and uncertain weather patterns. Conventional accident prediction models use static historical data, which does not enable them to be responsive to real-time traffic patterns. This research is responding to the demand for predictive models that are adaptive, smart, and can facilitate smart city infrastructure through real-time data integration. Machine learning models—Multilayer Perceptron, Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors—were tested with both historical and real-time traffic data. The models were optimized and trained on varied datasets to improve prediction accuracy. Of these, Gradient Boosting recorded the highest accuracy at 88.1%, followed by Random Forest at 83.73%, showing the power of ensemble learning techniques in predicting accidents. This research emphasizes the significance of real-time data integration for accident prediction and prevention. Merging environmental elements like weather conditions and traffic congestion improves prediction quality, allowing proactive prevention of accidents. By leveraging these models, cities can shift towards data-based road management, infrastructure planning, and congestion control. The results show that real-time models of accident prediction have the potential to enhance urban safety, opening the door to smarter and more efficient traffic management systems. © Authors.
Author Keywords	Gradient Boosting; K-Nearest Neighbors; Machine learning; Multilayer Perceptron; Random Forest; Support Vector Machine

Similar Articles

Id	Similarity	Authors	Title	Published
26856	0.914	Angadi V.S.; Halyal S.	Forecasting Road Accidents Using Deep Learning Approach: Policies To Improve Road Safety	Journal of Soft Computing in Civil Engineering, 8, 4 (2024)
44461	0.901	Abraham A.; Math C.B.; Prasad S.; Sharma M.	Real-Time Traffic Accident Detection For An Intelligent Mobility In Smart Cities	Signals and Communication Technology, Part F1293 (2024)
58188	0.899	Abi-Char P.E.; Ashtaiwi A.	Towards Intelligent And Dynamic Road Speed Adaptation Model In Smart Cities	International Conference on Wireless and Mobile Computing, Networking and Communications, 2020-October (2020)
934	0.897	Bakir D.; Moussaid K.; Chiba Z.; Abghour N.	A Comprehensive Review Of Traffic Congestion Prediction Models: Machine Learning And Statistical Approaches	2024 IEEE International Conference on Computing, ICOCO 2024 (2024)
47194	0.896	Almeida A.; Fonseca J.; Rasinhas P.; Costa C.; Paiva D.; Silva G.; Rito P.; Sargento S.	Safe Roads: Traffic Management And Road Safety Platform For Smart Cities	2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023 (2023)
42891	0.896	Mirza N.M.; Ali A.; Shifa N.; Ishak M.K.; Ammar K.	Predictive Modeling For Smart Traffic Systems: Harnessing Iot Data Insights	2023 24th International Arab Conference on Information Technology, ACIT 2023 (2023)
31957	0.895	Guru Vimal Kumar M.; Jude Moses Anto Devakanth J.; Selvapandian D.; Venkatesan R.; Revathi J.; Aruna R.	Integrating Cloud-Based Data Mining Algorithms For Smart City Infrastructure Management And Decision Support Systems	Proceedings - 2024 4th International Conference on Soft Computing for Security Applications, ICSCSA 2024 (2024)
60223	0.894	Tsalikidis N.; Mystakidis A.; Koukaras P.; Ivaškevičius M.; Morkūnaitė L.; Ioannidis D.; Fokaides P.A.; Tjortjis C.; Tzovaras D.	Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data And Weather Information	Smart Cities, 7, 1 (2024)
9472	0.894	Harimanto F.P.; Andrew C.; William H.; Asy'ari M.Z.; Artanto S.A.; Puji M.N.	Analyze And Predict Car Accidents Using Different Machine Learning Algorithms	Proceedings of 2023 International Conference on Information Management and Technology, ICIMTech 2023 (2023)
34277	0.894	Soedirman S.; Liu F.; Fan Z.; Zhang W.	It Is About Weather: Explainable Machine Learning For Traffic Accident Understanding	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (2023)