This project aims to create an innovative tollgate traffic prediction system using cutting-edge machine learning (ML) algorithms, including RF (Random Forest), DT (Decision Trees), and boosting techniques. Contrasting traditional methods, it covers all types of vehicles, like cars, buses, and 4-wheelers, giving us a complete picture of traffic dynamics. We start by gathering and preprocessing data carefully, making sure to include various vehicle types that you might see in the real world. We then turn this raw data into meaningful predictors, setting the stage for our robust predictive models. We use different algorithms like GBR (Gradient Boosting Regressor), Lasso, and Ridge to find patterns in the data, and a comparative analysis helps us understand how well each one works for different types of vehicles. We find that ensemble methods are particularly effective for tollgate traffic prediction, showing us the strengths and weaknesses of each algorithm. Besides just predicting traffic, this study can help with transportation planning and developing better infrastructure by giving us insights into what types of vehicles use tollgates. This research advances our understanding of tollgate traffic prediction systems and sets the stage for future innovations in smart transportation. This can help policymakers, urban planners, and researchers navigate the challenges of modern transportation. Overall, this project improves our ability to predict tollgate traffic and contributes to making our roads safer and more efficient for everyone.
Integrated Traffic Forecasting System Using Machine Learning
Lect. Notes in Networks, Syst.
International Conference on Soft Computing and Signal Processing ; 2024 ; Hyderabad, India June 20, 2024 - June 21, 2024
2025-05-25
14 pages
Article/Chapter (Book)
Electronic Resource
English
Pattern , Anomaly detection , Hyperparameters , Traffic , Real-time data , Regression , Prediction , Methods , Integration , Time series , Feature Signal, Image and Speech Processing , Cyber-physical systems, IoT , Artificial Intelligence , Computational Intelligence , Engineering , Professional Computing
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