Traffic Flow Prediction Using Machine Learning and Deep Learning Techniques: A Web-Based Application

Abstract

Rapidurbanizationandincreasingvehicledensity havesignificantlyintensifiedtrafficcongestionin metropolitan and semi-urban regions. Efficient trafficmanagementrequiresaccurateshort-term and long-term traffic flow prediction to support intelligent transportation systems. This paper presents a web-based traffic flow prediction system built using Long Short-Term Memory (LSTM)neuralnetworks.Thesystemistrainedon city-wisehistoricaltrafficdatasetsandiscapable of learning complex temporal dependencies presentintrafficpatterns.MultipleLSTMmodels are developed and trained for different cities, including Kalaburagi, Delhi, Mumbai, Pune, Hyderabad, and Kolkata. A Flask-based backend integrates the trained models with a lightweight webinterface,enablinguserstoobtainreal-time traffic flow predictions. Experimental results demonstrate that the proposed LSTM-based approach achieves superior prediction accuracy compared to conventional machine learning methods,highlightingitssuitabilityforsmartcity trafficplanningandcongestionmitigation

Introduction

This study presents an LSTM-based Traffic Flow Prediction System designed to improve traffic management in urban areas. Rapid population growth, increasing vehicle ownership, and limited road infrastructure have made traffic congestion a major challenge, leading to economic losses, higher fuel consumption, pollution, and commuter inconvenience. Accurate traffic prediction is essential for Intelligent Transportation Systems (ITS) to support route optimization, traffic control, and infrastructure planning.

Traditional prediction methods such as linear regression, ARIMA, SVM, and ANN often struggle to capture the complex and time-dependent nature of traffic data. To address these limitations, the proposed system uses Long Short-Term Memory (LSTM) neural networks, which are effective at learning long-term temporal patterns in sequential traffic data.

The system consists of three main components: a web-based user interface developed with HTML, CSS, and JavaScript; a Flask-based backend for data processing and model management; and a PyTorch-based LSTM prediction layer for forecasting traffic flow. Historical traffic data are preprocessed, normalized, and converted into sequences for training city-specific LSTM models. Users can enter parameters such as city, date, and time through the web interface and receive real-time traffic flow predictions.

The workflow includes data collection, preprocessing, sequence generation, model training, user input processing, and prediction display. The LSTM architecture contains input layers, stacked LSTM layers, dropout layers to reduce overfitting, and dense output layers for prediction.

Experimental results show that the proposed LSTM models accurately capture temporal traffic patterns and outperform traditional machine learning techniques by reducing prediction errors and improving robustness. The web-based deployment enhances accessibility and supports real-time traffic forecasting, making the system practical, scalable, and suitable for smart city transportation applications.

Conclusion

This paper presented a web-based traffic flow prediction system using Long Short-Term Memory networks. By leveraging deep learning techniques and city-specific historical datasets, the proposed system achieves accurate and reliable traffic forecasting. The integration of LSTMmodelswithaFlask-basedwebapplication enables real-time prediction and user accessibility, supporting intelligent transportationsystemsandsmartcityinitiatives.

References

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Copyright

Copyright © 2026 Sonal M Sangapur, Salila Punneshetty , Swati Jagannath, Prof. Ashok Patil. 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.