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A Real-Time Traffic Congestion Prediction System Using Weather and Temporal Data
Subject area: Science,Engineering and Technology · Area of research: Machine Learning for Traffic Prediction
DOI: https://doi.org/10.64388/IREV9I9-1715492
Abstract
Urban traffic congestion directly impacts travel time, fuel consumption, and commuter safety. SmartFlow AI is an intelligent web-based system that predicts traffic congestion levels by integrating machine learning, live weather data, and real-time route mapping. A classification model trained on historical traffic and environmental data predicts congestion as High, Moderate, or Low using inputs such as day of week, time of travel, traffic volume, and weather factors including temperature, humidity, and rainfall. Built on Streamlit, the system accepts source and destination locations, applies Nominatim geocoding, OSRM routing, and OpenWeatherMap for live weather retrieval. When high congestion is detected, alternative routes are automatically recommended and visualized using interactive Folium maps. SmartFlow AI combines traffic prediction, weather analysis, and route optimization into a single accessible platform to enhance commuter decision-making.
Keywords
Traffic Congestion Prediction, Machine Learning, Real-Time Data Processing, Intelligent Transportation Systems, Route Optimization, Geospatial Computing, Weather-based Traffic Analysis, OSRM Routing Engine, Urban Mobility, Streamlit Web Application
How to cite this paper
@article{1715492,
author = {D. Thepaak Raajhan, Dr. S. Parthasarathy},
title = {A Real-Time Traffic Congestion Prediction System Using Weather and Temporal Data},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2193-2199},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715492.pdf},
abstract = {Urban traffic congestion directly impacts travel time, fuel consumption, and commuter safety. SmartFlow AI is an intelligent web-based system that predicts traffic congestion levels by integrating machine learning, live weather data, and real-time route mapping. A classification model trained on historical traffic and environmental data predicts congestion as High, Moderate, or Low using inputs such as day of week, time of travel, traffic volume, and weather factors including temperature, humidity, and rainfall. Built on Streamlit, the system accepts source and destination locations, applies Nominatim geocoding, OSRM routing, and OpenWeatherMap for live weather retrieval. When high congestion is detected, alternative routes are automatically recommended and visualized using interactive Folium maps. SmartFlow AI combines traffic prediction, weather analysis, and route optimization into a single accessible platform to enhance commuter decision-making.},
keywords = {Traffic Congestion Prediction, Machine Learning, Real-Time Data Processing, Intelligent Transportation Systems, Route Optimization, Geospatial Computing, Weather-based Traffic Analysis, OSRM Routing Engine, Urban Mobility, Streamlit Web Application},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715492}
}