METHOD FOR AI-BASED TRAFFIC PREDICTION AND MANAGEMENT

Abstract

The present invention provides an AI-based method and system for traffic prediction and management, utilizing real-time data from various sources such as traffic sensors, cameras, GPS devices, and social media feeds. The system employs advanced machine learning algorithms to analyze traffic patterns, predict congestion, and identify potential disruptions. Based on these predictions, the system dynamically adjusts traffic control mechanisms, such as traffic signal timings, and provides real-time route recommendations to drivers. The system continuously learns and adapts from new data, improving prediction accuracy over time, thereby optimizing traffic flow, reducing congestion, enhancing road safety, and contributing to environmental sustainability.

Specification

Description:The embodiments of the present invention generally relates to intelligent transportation systems, and more specifically, to a method and system for AI-based traffic prediction and management. It leverages real-time data collection, machine learning algorithms, and dynamic traffic control mechanisms to optimize traffic flow, reduce congestion, and improve road safety in urban and suburban environments.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Traffic congestion is a widespread problem in urban areas, causing delays, environmental pollution, and increased fuel consumption. Traditional traffic manag , Claims:1. A method for AI-based traffic prediction and management, comprising the steps of:
collecting real-time traffic data from a plurality of data sources;
pre-processing the collected data to remove noise and normalize the data;
analyzing the preprocessed data using machine learning algorithms to detect traffic patterns;
predicting future traffic conditions based on the analyzed data; and
adjusting traffic control mechanisms or providing route recommendations based on the predicted traffic conditions.

2. The method of claim 1, wherein the machine learning algorithms include at least one of supervised learning, reinforcement learning, or deep learning models.

3. The method of claim 1, wherein the traffic data is collected from at least one of traffic sensors, GPS devices, cameras, and social media feeds.

4. The method of claim 1, wherein the traffic control mechanisms include traffic signal adjustments, traffic light sequencing, and autonomous vehicle route adjustments.

5. The method of claim 1, further comp

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