AI-BASED TRAFFIC MANAGEMENT SYSTEM

Abstract

Abstract The present invention discloses an AI-based Traffic Management System presents a solution to the growing challenge of traffic congestion in urban environments. By integrating advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Field-Programmable Gate Arrays (FPGA), the system effectively optimizes traffic flow and enhances the prioritization of emergency vehicles. This present invention not only improves overall traffic efficiency but also ensures faster response times for emergency services, contributing to safer roads for all users. As cities continue to expand and traffic demands increase, this system stands as a vital tool for creating smarter, more responsive urban transportation networks that prioritize public safety and mobility

Specification

Description:AI-BASED TRAFFIC MANAGEMENT SYSTEM

Field of Invention
The present invention relates to a traffic management system. More particularly, an AI-based traffic management system.

Background of the Invention
Urban traffic congestion has become a critical issue in modern cities, leading to increased travel times, environmental pollution, and safety hazards. Traditional traffic management systems often rely on fixed timing patterns and reactive measures, which fail to adapt to real-time traffic conditions. As cities grow and vehicular traffic increases, there is an urgent need for intelligent solutions that can dynamically manage traffic flow and prioritize emergency vehicles
US10867512B2 discloses a system controlling CAVs by sending individual vehicles with customized, detailed, and time-sensitive control instructions and traffic information for automated vehicle driving, such as vehicle following, lane changing, route guidance, and other related information.
"Review of Intelligent Traffic Management Systems" - IEEE Transactions on Intelligent Transportation Systems.
"Adaptive Traffic Signal Control Systems: A Review" - Transportation Research Part C: Emerging Technologies.
The present invention overcomes from the drawbacks of the prior arts by integrating seamlessly with existing city infrastructure, allowing for a comprehensive traffic management solution that not only addresses current congestion challenges but also adapts to future urban mobility needs.
disclosing.

Objectives of the Invention
The prime objective of the present invention is to provide an AI-based traffic management system.

Another object of this invention is to provide the AI-based traffic management system that leverages Field-Programmable Gate Array (FPGA) controller to optimize traffic control at intersections.
Another object of this invention is to provide the AI-based traffic management system that identify and prioritize emergency vehicles, such as ambulances, fire trucks, and police cars, through unique RFID tags.
Another object of this invention is to provide the AI-based traffic management system that employs real-time data capturing from cameras to assess vehicle density and predict traffic patterns using a machine learning method.
Yet another object of this invention is to provide the AI-based traffic management system that integrates with emergency services, prioritizing emergency vehicles for faster routes and improving response times, additionally, it promotes public transport by integrating buses and metros to reduce private vehicle use, reroute traffic during accidents or blockages, minimizing disruptions, while a feedback mechanism allows user input for continuous improvement.
These and other objects of the present invention will be apparent from the drawings and descriptions herein. Every object of the invention is attained by at least one embodiment of the present invention.

Summary of the Invention
In one of the aspects of the invention, it provides an AI-based traffic management system that leverages Field-Programmable Gate Array (FPGA) controller to optimize traffic control at intersections.
In one of the aspects of the present invention, the system employs real-time data captured from cameras to assess vehicle density and predict traffic patterns using a machine learning method; by processing this data on the edge, the FPGA controller adjusts the traffic light timings dynamically, ensuring efficient traffic flow and reducing congestion.
In one of the aspects, the present invention ensures smooth traffic flow and prevents congestion, with a modular design enabling scalability as traffic volumes increase, machine learning method predict traffic patterns, optimizing light timings and improving overall traffic management, while adaptive algorithms refine system responses based on real-time data.
Brief Description of Drawings
The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present disclosure. Further objectives and advantages of this invention will be more apparent from the ensuing description when read in conjunction with the accompanying drawing and wherein:
Figure 1 illustrates the Block diagram according to preferred embodiment of the present invention.
Figure 2 illustrates the data flow diagram according to an embodiment of the present invention.
DETAIL DESCRIPTION OF INVENTION
Unless the context requires otherwise, throughout the specification which follow, the word "comprise" and variations thereof, such as, "comprises" and "comprising" are to be construed in an open, inclusive sense that is as "including, but not limited to".
In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

The headings and abstract of the invention provided herein are for convenience only and do not interpret the scope or meaning of the embodiments. Reference will now be made in detail to the exemplary embodiments of the present invention.
The present invention discloses an AI-based traffic management system that is implemented using FPGA controller and addresses the growing challenges in urban transportation and infrastructure. By deploying a network of cameras for real time vehicle detection and speed monitoring, the system identifies vehicles, reads number plates, and detects traffic violations such as over speeding, improper helmet use, and illegal parking. Violations are recorded, and automatic challans are issued, streamlining enforcement while ensuring data security through robust anonymization measures to maintain privacy and user trust.
In describing the preferred embodiment of the present invention, reference will be made herein to like numerals refer to like features of the invention.
According to preferred embodiment of the invention, referring to Figure 1, the AI-based traffic management system enhances urban traffic flow and prioritizes emergency vehicles through the integration of key components:
AI Algorithm: - Utilizes high-resolution cameras to detect and count vehicles in real-time, assessing traffic density for dynamic management. AI algorithms are made using python programming.
ML Methods: - Predicts traffic conditions based on data from the AI algorithm, providing insights for optimal traffic light timings to manage congestion proactively.
FPGA Controller: - Processes inputs from the ML methods to control traffic lights in real-time, ensuring smooth traffic flow and minimizing delays.
Emergency Vehicle Detection: - Detects emergency vehicles using unique RFID tags, prioritizing their passage by adjusting traffic signals to improve response times.
Citywide Integration: - Communicates with adjacent intersections to share real-time data, allowing for synchronized traffic light adjustments and providing the fastest routes for emergency vehicles. This streamlined overview succinctly captures the system's core components and functionalities, highlighting its innovative approach to traffic management and emergency response.
According to another embodiment of the invention, referring Figure 2, the system works in the following steps:
• Initially, the user request to the Traffic management system (TMS) for the traffic data;
• Thew TMS than sends the real time traffic data to the user,
• In case, if user experience any traffic violation, then the user captures the violation footage from the camera and store it at the data security module;
• In case, if there is any traffic jam, then user request for the alternative route to TMS;
• The TMS provides the suggestions for alternative routes to the user;
• If any incident is identified at that particular route, then the user request for incident management;
• The TMS will automatically reroute for the incident;
• the user will provide the feedback on system performance and adjustments, if require and request environment impact monitoring from TMS;
• and finally, the TMS send environment impact data and suggestions for eco-friendly practices.
According to another embodiment of the invention, the AI-based traffic management system predicts the traffic through machine learning (ML). The ML, is a critical component of the AI based Traffic Management System, utilizing real-time vehicle density data from the AI algorithm to predict traffic conditions at intersections. It classifies traffic into three categories: high, normal, and low enabling the FPGA controller to optimize traffic light timings effectively. Its working is in the following steps:
• Data Pre-processing and Feature Engineering: the ML model begins with pre-processing the vehicle density data, which includes cleaning, handling missing values, and extracting relevant features such as time of day, day of the week, weather conditions, and historical traffic patterns. This process enhances the model's predictive capabilities;
• Traffic Condition Classification: Using techniques like decision trees or gradient boosting, the ML classifies traffic conditions based on predefined thresholds. It continuously updates its classifications as new data is received, ensuring real-time accuracy
• Temporal and Spatial Considerations: to capture the dynamic nature of traffic, the model incorporates both temporal and spatial dependencies. It considers historical data and the interconnectedness of intersections, providing comprehensive predictions that reflect the impact of traffic in one area on adjacent areas;
• Continuous Learning and Adaptation: the ML is designed for continuous learning, refining its algorithms and parameters as new data becomes available. This adaptability ensures that the ML remains effective in responding to changing traffic patterns and environmental factors.
According to another embodiment of the invention, in the AI-based traffic management system, the FPGA controller is the center of the System, responsible for real-time traffic signal control and optimization based on ML model predictions. It functions in the following manner:
• Real-Time Control: the FPGA processes ML input on traffic conditions, making instantaneous decisions on light phases and durations to adapt to changing conditions and ensure smooth flow;
• Traffic Optimization: by dynamically adjusting timings, the FPGA prioritizes main roads during peaks while allowing adequate side road green time during offpeaks, minimizing waiting and reducing congestion;
• Emergency Response: when the system detects emergency vehicles via RFID, the FPGA immediately alter light sequences to prioritize their passage, ensuring quick navigation for responders;
• Integration and Scalability: the FPGA coordinates with the AI algorithm and ML model, communicating with adjacent intersections for coordinated adjustments. Its scalability allows adapting to evolving traffic patterns and integrating new features over time.
According to another embodiment of the invention, in the AI-based traffic management system, the emergency vehicle detection and prioritization enhances urban responsiveness to emergencies. It uses Radio Frequency Identification (RFID) to identify and prioritize emergency vehicles, such as ambulances and fire trucks. The emergency vehicle detection and prioritization are performed in the following manner:
• RFID Detection: emergency vehicles equipped with unique RFID tags emit signals detected by RFID readers at intersections. When an emergency vehicle approaches, the system quickly recognizes the signal and triggers a rapid response;
• Traffic Clearance Protocol: upon detection, the system adjusts traffic light sequences across intersections, allowing the emergency vehicle to pass without delay. The FPGA controller ensures that red lights turn green in a coordinated manner;
• Citywide Coordination: the system synchronizes with multiple crossings to optimize traffic light adjustment;
• Enhanced Public Safety: this prioritization significantly reduces response times for emergency services, ensuring quick access to critical situations;
• Continuous Monitoring: the system continuously monitors traffic conditions, allowing for real time adaptations and rerouting as needed.
According to another embodiment of the invention, in the AI-based traffic management system, the citywide Integration ensures coordinated responses across multiple intersections, enhancing traffic management during emergencies. The citywide Integration is performed in the following manner:
• Synchronized Response: when an emergency vehicle is detected, the system shares real-time data with neighbouring crossings. This allows adjacent intersections to adjust their traffic light timings in anticipation of the emergency vehicle's passage;
• Dynamic Traffic Light Adjustments: by synchronizing signals across intersections, the system enables a coordinated response, ensuring that red lights turn green to facilitate the emergency vehicle's swift navigation through the city;
• Enhanced Traffic Flow: this integration not only benefits emergency vehicles but also improves overall traffic flow by optimizing signal timings for all vehicles, reducing wait times and congestion;
• Continuous Data Sharing: the continuous data sharing among intersections allows the system to adapt to changing traffic patterns and emergency situations in real time, maintaining efficiency.
According to another embodiment of the invention, in the AI-based traffic management system optimizes traffic flow and enhances the prioritization of emergency vehicles, improves overall traffic efficiency but also ensures faster response times for emergency services, contributing to safer roads for all users.
Although a preferred embodiment of the invention has been illustrated and described, it will at once be apparent to those skilled in the art that the invention includes advantages and features over and beyond the specific illustrated construction. Accordingly, it is intended that the scope of the invention be limited solely by the scope of the hereinafter appended claims, and not by the foregoing specification, when interpreted in light of the relevant prior art.
, Claims:We Claim;
1. An AI-based traffic management system comprises of an AI algorithm, a machine learning (ML) method, a FPGA Controller, an emergency vehicle detection and a citywide integration, wherein,
• The AI algorithm: utilizes high-resolution cameras to detect and count vehicles in real-time, assessing traffic density for dynamic management;
• The ML method predicts traffic conditions based on data from the AI algorithm, providing insights for optimal traffic light timings to manage congestion proactively;
• The FPGA controller processes inputs from the ML methods to control traffic lights in real-time, ensuring smooth traffic flow and minimizing delays;
• The emergency vehicle detection detects emergency vehicles using unique RFID tags, prioritizing their passage by adjusting traffic signals to improve response times;
• The citywide integration communicates with adjacent intersections to share real-time data, allowing for synchronized traffic light adjustments and providing the fastest routes for emergency vehicles.
2. The AI-based traffic management system as claimed in claim 1, wherein the system works in the following steps:
• Initially, the user request to the Traffic management system (TMS) for the traffic data;
• Thew TMS than sends the real time traffic data to the user,
• In case, if user experience any traffic violation, then the user captures the violation footage from the camera and store it at the data security module;
• In case, if there is any traffic jam, then user request for the alternative route to TMS;
• The TMS provides the suggestions for alternative routes to the user;
• If any incident is identified at that particular route, then the user request for incident management;
• The TMS will automatically reroute for the incident;
• the user will provide the feedback on system performance and adjustments, if require and request environment impact monitoring from TMS;
• and finally, the TMS send environment impact data and suggestions for eco-friendly practices.
3. The AI-based traffic management system as claimed in claim 1, wherein the system predicts the traffic through machine learning (ML) method utilizing real-time vehicle density data from the AI algorithm to predict traffic conditions at intersections; classifying traffic into three categories: high, normal, and low enabling the FPGA controller to optimize traffic light timings effectively.

4. The AI-based traffic management system as claimed in claim 1, wherein the FPGA controller functions in the following manner:
• the FPGA controller performs real time control of traffic by processing the ML input on traffic conditions, making instantaneous decisions on light phases and durations to adapt to changing conditions and ensure smooth flow;
• the traffic optimization by dynamically adjusting timings, the FPGA prioritizes main roads during peaks while allowing adequate side road green time during offpeaks, minimizing waiting and reducing congestion;
• when the system detects emergency vehicles via RFID, the FPGA immediately alter light sequences to prioritize their passage, ensuring quick navigation for responders;
• the FPGA coordinates with the AI algorithm and ML model, communicating with adjacent intersections for coordinated adjustments.
5. The AI-based traffic management system as claimed in claim 1, wherein the emergency vehicle detection and prioritization is performed in the following manner:
• The emergency vehicles equipped with unique RFID tags emit signals detected by the RFID readers at intersections, when an emergency vehicle approaches, the system quickly recognizes the signal and triggers a rapid response;
• Traffic Clearance Protocol: upon detection, the system adjusts traffic light sequences across intersections, allowing the emergency vehicle to pass without delay, the FPGA controller ensures that red lights turn green in a coordinated manner;
• Citywide Coordination: the system synchronizes with multiple crossings to optimize traffic light adjustment;
• Enhanced Public Safety: this prioritization significantly reduces response times for emergency services, ensuring quick access to critical situations;
• Continuous Monitoring: the system continuously monitors traffic conditions, allowing for real time adaptations and rerouting as needed.
6. The AI-based traffic management system as claimed in claim 1, wherein the citywide Integration is performed in the following manner:
• Synchronized Response: when an emergency vehicle is detected, the system shares real-time data with neighbouring crossings, allowing adjacent intersections to adjust their traffic light timings in anticipation of the emergency vehicle's passage;
• Dynamic Traffic Light Adjustments: by synchronizing signals across intersections, the system enables a coordinated response, ensuring that red lights turn green to facilitate the emergency vehicle's swift navigation through the city;
• Enhanced Traffic Flow: this integration not only benefits emergency vehicles but also improves overall traffic flow by optimizing signal timings for all vehicles, reducing wait times and congestion;
• Continuous Data Sharing: the continuous data sharing among intersections allows the system to adapt to changing traffic patterns and emergency situations in real time, maintaining efficiency.

7. The AI-based traffic management system as claimed in claim 1, wherein the system identifies vehicles, reads number plates, and detects traffic violations such as over speeding, improper helmet use, and illegal parking.

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