To achieve this, the project may use various techniques such as machine learning algorithms, data analytics, and real-time traffic monitoring. The system can analyze various factors such as traffic patterns, road conditions, and weather forecasts to predict the most efficient route.
Traffic Prediction Using Machine Learning - ResearchGate
Jan 1, 2022 · Machine Learning (ML) models such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown great promise in traffic prediction.
Accordingly, this study presents the development and implementation of a new architecture to predict the traffic flow in a city and the strategy used in this scheme. The proposal considers the use of machine learning, computer vision, deep learning, and neuronal networks to …
System architecture for the traffic congestion prediction model.
The study by [14] proposed a traffic congestion prediction model using machine learning techniques used for the prediction of the traffic congestion existence in LTE networks as...
Intelligent transportation system is used for analyzing the information. ITS is used to control communication technologies for road transportation to improve safety and efficiency.
Traffic Prediction for Intelligent Transportation System using Machine ...
Feb 1, 2020 · The final goal is to create a viable machine learning program capable of anticipating intelligent transit traffic utilizing information such as GPS, speed, direction, and start-end time. ...
Traffic Prediction Using Machine Learning - Tpoint Tech - Java
Mar 17, 2025 · There are several types of machine learning algorithms that can be used for traffic prediction, including regression, time-series analysis, and artificial neural networks. Regression models use historical traffic data to predict future traffic conditions based on past trends.
Deep neural networks, a distributed random forest, a gradient boosting machine, and a generalized linear model were all examined as predictive methods for traffic flow prediction.
Given the substantial volume of available traffic data, the project proposes the use of machine learning, genetic algorithms, soft computing, and deep learning algorithms to analyze transportation big data with minimal reductions.
Traffic Prediction System Using Machine Learning Algorithms
Jul 6, 2021 · In this paper, some of the common and familiar machine learning concepts like Deep Autoencoder (DAN), Deep Belief Network (DBN), and Random Forest (RF) are applied on the online dataset for the traffic flow predictions.