Traffic Prediction System Using Machine Learning

Abstract

The rapid growth of urbanization has led to increased traffic congestion, posing significant challenges in efficient transportation management. This project proposes a machine learning-based intelligent traffic prediction system that utilizes both historical and real-time data to forecast traffic conditions accurately. By integrating data from Google Maps API, OpenWeatherMap API, and road sensors, the system analyzes key factors such as time, weather, vehicle density, and road type to predict congestion levels as low, moderate, or high. Advanced algorithms such as ARIMA, Regression, and Long Short-Term Memory (LSTM) are employed to model time-dependent traffic patterns and generate precise forecasts. The system’s results are visualized through an interactive web-based dashboard, providing real-time congestion insights, alerts, and alternative route suggestions for commuters and traffic authorities. This integrated and data-driven approach enhances urban mobility, reduces travel time and fuel consumption, and supports intelligent city planning by transforming traditional reactive systems into proactive, predictive traffic management solutions.

Introduction

Traffic congestion is a major urban challenge caused by population growth, increased vehicles, and insufficient infrastructure, leading to delays, pollution, and reduced productivity. Traditional traffic management systems are largely reactive and cannot adapt to real-time conditions, highlighting the need for intelligent, data-driven solutions. The proposed Traffic Prediction System Using Machine Learning leverages historical and real-time data from sources like Google Maps API, OpenWeatherMap API, and road sensors to forecast congestion levels as low, moderate, or high.

The system employs advanced preprocessing, including handling missing values, normalization, feature encoding, and data augmentation, to ensure clean, consistent input for machine learning models. It integrates algorithms such as ARIMA for time-series trends, Regression models for variable relationship analysis, and LSTM networks for capturing temporal dependencies. A Flask-based backend powers a web dashboard that visualizes traffic predictions, alerts users, and suggests alternate routes.

By combining predictive modeling with real-time visualization, the system transforms reactive traffic control into proactive congestion management, enhancing commuter experience, supporting smart city initiatives, and enabling sustainable urban transportation planning.

Conclusion

The proposed Traffic Prediction System Using Machine Learning presents a highly accurate and efficient approach for forecasting traffic congestion based on historical and real-time data. By leveraging advanced ML algorithms such as ARIMA, Regression, and LSTM, the system effectively learns temporal traffic patterns and predicts congestion levels across various routes and time intervals. The integration of real-time APIs (Google Maps and OpenWeatherMap) ensures that the model adapts dynamically to changing traffic and weather conditions, providing reliable and timely predictions. The system’s visualization dashboard enables easy interpretation of traffic trends, supporting data-driven decision-making for both commuters and traffic authorities. Experimental evaluations demonstrate that the LSTM model achieved an overall accuracy of 96.7%, outperforming traditional statistical and regression-based models in terms of precision, adaptability, and robustness. This confirms the system’s suitability for deployment in smart city environments to improve traffic flow, reduce congestion, and enhance commuter experience. In the future, the framework can be extended by integrating IoT-based sensors, real-time camera feeds, and deep learning architectures such as CNNs or Graph Neural Networks to capture complex spatial-temporal dependencies. Additionally, cloud-based deployment can enhance scalability and performance, making the system a powerful tool for intelligent transportation and sustainable urban mobility.

References

[1] Y. Li, H. Zhang, and X. Wang, “Traffic Flow Forecasting with Deep Learning: A Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 6, pp. 11245–11260, 2023. [2] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long Short-Term Memory Neural Network for Traffic Speed Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 2103–2112, 2022. [3] S. Vlahogianni, M. Karlaftis, and J. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transportation Research Part C: Emerging Technologies, vol. 43, pp. 3–19, 2014. [4] B. Lv, Y. Chen, and X. Li, “An Improved ARIMA Model for Traffic Flow Prediction,” Procedia Engineering, vol. 137, pp. 818–827, 2016. [5] Y. Tian and L. Pan, “Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network,” Proceedings of the IEEE International Conference on Smart City/SocialCom/SustainCom, pp. 153–158, 2015. [6] J. Gu, Y. Sun, and B. Chen, “Real-Time Urban Traffic Prediction with Multi-Source Data,” IEEE Access, vol. 8, pp. 127237–127247, 2020. [7] Google Maps API Documentation – “Google Maps Platform Traffic Layer,” [Online]. Available: https://developers.google.com/maps/documentation. [8] OpenWeatherMap API Documentation – “Weather Data for Developers,” [Online]. Available: https://openweathermap.org/api. [9] H. Zheng, Y. Yao, and W. Zhang, “Short-Term Traffic Volume Forecasting: A Comparison of ARIMA and LSTM Models,” Journal of Advanced Transportation, vol. 2019, Article ID 9579407, pp. 1–14, 2019. [10] R. Chien and C. Ding, “Dynamic Traffic Prediction Using Machine Learning Techniques,” International Journal of Computer Applications, vol. 182, no. 36, pp. 15–21, 2020. [11] M. Benrhmach, M. Ouladsine, and M. Fakhouri, “Real-Time Traffic Congestion Prediction Using Deep Learning,” IEEE International Conference on Artificial Intelligence and Computer Vision (AICV), pp. 123–131, 2021. [12] K. Zhang, W. Luo, and F. Wu, “Big Data-Driven Urban Traffic Flow Prediction with Spatio-Temporal Graph Neural Network,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 5, pp. 4270–4283, 2023. [13] L. Yu, J. Wang, and R. Lai, “Forecasting Short-Term Traffic Flow with Deep Learning: A Case Study on Highway Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4263–4274, 2021.

Copyright

Copyright © 2025 K. Jeyan, J. Joel Lenson, N. Nazmunisha. 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.