Improved Road Traffic Congestion Prediction Using Machine Learning through Modified Index

DOI

10.2991/978-94-6463-716-8_13How to use a DOI?

Keywords

Congestion Prediction; Modified Congestion Index; Temporal Features

Abstract

Accurate traffic congestion forecasting is an indispensable element of urban transport systems. This paper suggests a machine learning model to predict rush-hour traffic congestion using a newly defined Traffic Congestion Index (M_TCI), incorporating traffic density as a crucial factor for congestion prediction. This study uses XGBoost algorithm with spatio-temporal and contextual features such as holidays and seasonality to enhance the model’s accuracy. The model focuses on long-term prediction, incorporating the day of the week, time, holiday and seasonality to predict daily road network performance. Results show that the model outperforms ensemble models- CatBoost, Gradient Boosting Machine (GBM) and LightGBM and achieves an accuracy of 90%. XGBoost performs better in handling large and high-dimensional datasets, making it a valuable tool for predicting traffic congestion and optimizing urban road networks.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Deepti Soni
AU  - Shraddha Masih
PY  - 2025
DA  - 2025/05/26
TI  - Improved Road Traffic Congestion Prediction Using Machine Learning through Modified Index
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 150
EP  - 158
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_13
DO  - 10.2991/978-94-6463-716-8_13
ID  - Soni2025
ER  -