The Top-View Vehicle Detection Image Dataset for YOLOv8 is essential for tasks like traffic monitoring and urban planning. It provides a unique perspective on vehicle behavior and traffic …

Nov 9, 2020 · The ML model has great features for capturing complicate correlations, dealing with the non-linear vibration in time-series, and building up a complicated model structure. In this …

This study explores the use of Machine Learning (ML) to predict traffic conditions in real-time, enabling Intelligent Transportation Systems (ITS) to optimize traffic flow and reduce …

ABSTRACT- In this work, we deploy a real-time method for classifying vehicles and estimating their speeds using footage captured by traffic cameras along motorways. Basic techniques in …

Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specific traffic parameters like …

proposes a short-term traffic flow prediction model based on SVR and optimizes SVM parameters to form an improved SVR short-term traffic flow prediction model.

Jan 10, 2022 · In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive …

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 …

Mar 17, 2025 · With the availability of vast amounts of traffic data, machine learning algorithms can accurately predict traffic flow and congestion patterns in real time. These predictions can …

Dec 18, 2024 · Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries). Summary of open …