Deep Learning-Based Traffic Accident Prediction: An Investigative Study for Enhanced Road Safety




Traffic Prediction, Deep Learning, Convolutional Neural Network, Road Safety


INTRODUCTION: Traffic accidents cause enormous loss of life as well as property, which is a global concern. Effective accident prediction is essential for raising road safety and reducing the effects of accidents. To increase traffic safety, a deep learning-based technique for predicting accidents was developed in this research study.

OBJECTIVES: It gathers a large amount of data on elements including weather, road features, volume of traffic, and past accident reports. The dataset goes through pre-processing, such as normalization, to ensure that the scales of the input characteristics are uniform. Normalizing the gathered dataset ensures consistent scaling for the input features during the data processing step. This process enables efficient model training and precise forecasting. In order to track and examine the movement patterns of automobiles, people, and other relevant entities, real-time tracking and monitoring technologies, such as the deep sort algorithm, are also employed.

METHODS: The model develops a thorough grasp of the traffic situation by incorporating this tracking data with the dataset.  Convolutional Neural Networks (CNN), in particular, are utilized in this research for feature extraction and prediction. CNNs capture crucial road characteristics by extracting spatial features from images or spatial data. With its insights into improved road safety, this study advances the prediction of traffic accidents.

RESULTS: A safer transport infrastructure could result from the developed deep learning-based strategy, which has the potential to enable pre-emptive interventions, enhance traffic management, and eventually reduce the frequency and severity of traffic accidents.

CONCLUSION: The proposed CNN demonstrates superior accuracy when compared to the existing method.


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Song, X, Chen, K, Li, X, Sun, j, Hou, B, Cui, Y, Zhang, B, Xiong, G, Wang, Z. Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network, IEEE Transaction Intelligent Transportation System. 2021; Vol. 22, pp. 3285-3302. DOI:

Sathya, R, Ananthi S, Vaidehi K, A Hybrid Location-dependent Ultra Convolutional Neural Network-based Vehicle Number Plate Recognition Approach for Intelligent Transportation Systems, Concurrency and Computation: Practice and Experience, 2023; Vol.35:pp.1-25. DOI:

Balasubramaniam S, Joe V, Sivakumar TA: Optimization Enabled Deep Learning-Based DDoS Attack Detection in Cloud Computing. International Journal of Intelligent Systems. 2023; Vol.2023:pp.1-14. DOI:

Theofilatos, A. Incorporating Real-Time Traffic and Weather Data to Explore Road Accident Likelihood and Severity in Urban Arterials, Journal of Safety Research. 2017; Vol. 61, pp. 9-21. DOI:

Theofilatos, A, Chen, C, Antoniou, C. Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction, Transportation Research Record. 2019; Vol. 2673, pp. 169-178. DOI:

Ren, H, Song, Y, Wang, J, Hu, Y, Lei, J. A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction, Proceedings of IEEE International Conference on Intelligent Transportation Systems (ITSC). 2018; pp. 3346–3351. DOI:

Gutierrez-Osorio, C, Pedraza, C. Modern Data Sources and Techniques for Analysis and Forecast of Road Accidents: A Review, Journal of Traffic and Transportation Engineering. 2020; Vol. 7, pp. 432-446. DOI:

Das, S, Dutta, A, Avelar, R, Dixon, K, Sun, X, Jalayer, M. Supervised Association Rules Mining on Pedestrian Crashes in Urban Areas: Identifying Patterns for Appropriate Countermeasures, International Journal of Urban Science. 2019; Vol. 23, pp. 30-48. DOI:

Montella, A. A comparative analysis of Hotspot identification methods. Accident Analysis Prevention. 2010; Vol.42, pp. 571-581. DOI:

Kumar, R, Lokesh, K, Shankar, V, Kumar, S. An Improved Scalable Approach to Detect Black Spots on Roads Using DBSCAN Algorithm, Asian Journal of Information Technology. 2021; Vol. 20, pp. 160-167.

Hu, Y, Li, Y, Yuan, C, Huang, H. Modelling Conflict Risk with Real-Time Traffic Data for Road Safety Assessment: A Copula-Based Joint Approach, Transportation Safety Environment. 2022; Vol. 4, pp. 2641-4428. DOI:

Esenturk, E, Wallace, A, G, Khastgir, S, Jennings, P. Identification of Traffic Accident Patterns via Cluster Analysis and Test Scenario Development for Autonomous Vehicles, IEEE Access. 2022; Vol.10, pp. 6660-6675. DOI:

Kapania, S, Saini, D, Goyal, S, Thakur, N, Jain, R, Nagrath, P. Multi Object Tracking with UAVs Using Deep SORT and YOLOv3 Retina Net Detection Framework, in Proceedings of the 1st ACM Workshop on Autonomous and Intelligent Mobile Systems 2020; pp. 1-6. DOI:




How to Cite

G. M and D. V, “Deep Learning-Based Traffic Accident Prediction: An Investigative Study for Enhanced Road Safety”, EAI Endorsed Trans IoT, vol. 10, Feb. 2024.