IoT-based traffic monitoring system

Abstract

An IoT-based traffic monitoring system, consisting of: a variety of sensor units, each sensor unit comprising a video camera, an infrared sensor, a GPS module and an accelerometer; a local processing unit that is connected to each sensor unit and configured to filter and analyze raw traffic data; a central processing unit configured to receive processed data from the local processing unit, to further analyze the data using machine learning, and to optimize traffic control measures, wherein the central processing unit is configured to dynamically adjust traffic light sequences based on the analysis of real-time traffic conditions, thereby improving the efficiency of traffic flow at intersections. a communication framework that includes a wireless sensor network and a mobile ad-hoc network to transfer data between the sensor units and the central processing unit; a traffic management interface that provides traffic authorities with real-time traffic conditions and control functions; and an end-user application interface to provide real-time traffic information for commuters and service providers.

Description

Field of the invention:

[0001] The present invention relates to the field of intelligent transportation systems and, more particularly, to an IoT-based traffic monitoring system that enhances real-time traffic control, analysis, and reporting by utilizing connected devices and advanced data analytics. Background of the invention:

[0002] Traffic congestion has become a major problem in urban areas, leading to longer travel times, environmental pollution, and road safety concerns. Conventional traffic monitoring systems often rely on limited data sources, resulting in delayed responses to traffic conditions. The emergence of the Internet of Things (IoT) offers the opportunity to improve traffic monitoring through the integration of various sensors, devices, and analysis methods to facilitate real-time data collection and processing. The objective of this invention is to provide a comprehensive traffic monitoring system that utilizes IoT technologies to enable efficient traffic flow management and improve decision-making processes.

[0003] Increasing urbanization and population growth in cities worldwide have led to severe traffic congestion, posing significant challenges for urban planners and traffic authorities. For decades, traditional traffic monitoring systems have been used to manage vehicle flow and ensure road safety. They primarily utilize fixed traffic cameras, inductive loop sensors, and manual reporting methods. These systems have limited ability to provide real-time data, as they often rely on historical traffic patterns and assumptions rather than current conditions. Technological advances have brought new solutions, but many existing approaches still have critical limitations, necessitating the development of more advanced systems.

[0004] One of the most commonly used traffic monitoring methods currently is the use of fixed traffic cameras. While they provide a visual representation of traffic conditions, their functionality is often limited to simple observations. Many cameras are equipped only for basic video recording and require manual review for data analysis, leading to delayed responses to traffic incidents. Furthermore, their effectiveness decreases in adverse weather conditions or low light, as they may not capture accurate information. This reliance on human supervision can also lead to subjective interpretations of traffic incidents and cause inconsistencies in data reporting and analysis.

[0005] Inductive loop sensors embedded in the roadway are another common solution for monitoring vehicle counts and speeds. While these systems provide accurate data on vehicle presence and traffic flow rates, they have significant drawbacks. Their installation and maintenance are costly because they require excavating roads, causing traffic disruption during installation and potential wear over time. Furthermore, inductive loops can only detect vehicles directly overhead, creating blind spots during monitoring. They also provide little insight into the type or behavior of vehicles, which is increasingly necessary for effective traffic management.

[0006] Another approach involves using GPS data from mobile applications and navigation systems. This method has gained popularity because of its ability to collect data from a wide range of vehicles and provide insights into real-time traffic conditions. However, it has several limitations. First, the accuracy of GPS data can be affected by signal interference, particularly in urban environments with tall buildings that block satellite signals. Furthermore, relying on users to voluntarily share their location data creates information gaps, especially in areas where fewer users use navigation apps. This data also relies on the participation of a significant number of vehicles to provide a comprehensive view of traffic conditions, which is not always feasible.

[0007] The introduction of smart traffic lights that adapt based on real-time traffic data represents an advance in traffic management solutions. These systems often leverage data from existing sensors to dynamically adjust traffic light timing. While this technology improves traffic flow at specific intersections, it is typically limited to isolated applications and lacks a comprehensive overview of traffic patterns across larger areas. Furthermore, their reliance on fixed sensors can lead to inefficient responses to sudden changes in traffic flow, such as accidents or road closures, as they may lack access to a wider range of real-time data.

[0008] Another method being increasingly explored is the use of connected vehicles to improve traffic monitoring. Sensor-equipped vehicles can communicate their speed and location to traffic management systems, theoretically enabling more responsive traffic control. However, this solution depends on the widespread adoption of connected vehicle technologies, which is still in its early stages. There are also privacy concerns surrounding the continuous tracking of vehicles that could deter users from adopting such technologies.

[0009] In addition to these traditional and new methods, various software applications have been developed that provide traffic monitoring and management solutions. These applications often use a combination of user-reported data, historical trends, and sensor inputs to generate traffic forecasts and updates. However, the effectiveness of these applications varies greatly, depending on the accuracy of the user-provided data and the techniques used for analysis. Incorrect reports or insufficient user input can lead to misleading information and ultimately compromise the decision-making processes of traffic authorities and motorists.

[0010] The challenges posed by existing traffic monitoring solutions underscore the need for a more integrated and intelligent approach. An IoT-based traffic monitoring system has the potential to address these shortcomings by leveraging a network of interconnected devices to collect real-time data from multiple sources. This solution promises better granularity and accuracy of traffic data collection, providing traffic authorities with comprehensive insights into traffic conditions, patterns, and trends. The rapid growth of urbanization has led to an increase in car traffic, resulting in congestion, accidents, and inefficiencies in transportation systems. Conventional traffic monitoring solutions are often based on outdated technologies, offer limited data, and lack the ability to analyze information in real time.These systems generally require manual intervention to control traffic and cannot dynamically adapt to fluctuating traffic conditions. Summary of the invention:

[0011] The IoT-based traffic monitoring system comprises a network of interconnected sensors deployed throughout an urban area to collect traffic-related data such as vehicle counts, speeds, and environmental conditions. These sensors are connected to a central data processing unit via wireless communication protocols, enabling seamless data transfer and real-time monitoring. The data processing unit analyzes the collected information using machine learning techniques to identify traffic patterns, predict congestion, and optimize traffic lights accordingly. Furthermore, the system features a user interface that provides drivers with traffic updates and alerts via mobile applications, enabling informed travel decisions.

[0012] The primary objective of the invention is to develop an IoT-based traffic monitoring system that improves real-time traffic management and decision-making through the integration of advanced sensor technologies and data analytics. This system is intended to provide traffic authorities and users with comprehensive, accurate, and up-to-date traffic data, thus enabling efficient monitoring of vehicle flow, congestion patterns, and road conditions. Another objective is to utilize a network of interconnected devices to collect data from multiple sources, thus overcoming the limitations of traditional traffic monitoring solutions based on isolated sensors or manual reporting methods.By using machine learning techniques and advanced data processing techniques, the system attempts to analyze collected data in real time, enabling proactive traffic management and timely identification of traffic incidents or anomalies.

[0013] Furthermore, the invention aims to improve the user experience by providing real-time traffic updates and alerts via a dedicated mobile application. This goal includes enabling drivers to make informed travel decisions by offering dynamic route suggestions and notifications about congestion, accidents, or road closures. The system aims to improve traffic flow by enabling the dynamic adjustment of traffic lights based on real-time data, thereby reducing delays and improving overall road safety. Furthermore, the invention aims to create a scalable and adaptable framework that enables the seamless integration of additional sensors or devices as urban traffic conditions change.By achieving these goals, the IoT-based traffic monitoring system aims to contribute to the development of smarter, more efficient urban transport networks, ultimately leading to less congestion, better air quality, and a higher quality of life for city dwellers.

[0014] The present invention presents an IoT-based traffic monitoring system that enables real-time observation, analysis, and management of traffic conditions. This system consists of several hardware components, including a network of sensors, local processing units, a central processing unit, and a secure communication framework.

[0015] The sensors—consisting of video cameras, infrared sensors, GPS modules, and accelerometers—are strategically placed to collect extensive traffic data. This information is processed locally before being sent to a central cloud-based platform, where machine learning techniques evaluate traffic patterns and improve traffic flow. The system can dynamically adjust traffic control measures such as traffic lights and provide real-time notifications of accidents and congestion. It is designed to be scalable, secure, and energy-efficient, promoting sustainability and adaptability for smart city implementations. SHORT DESCRIPTION OF THE FIGURE

[0016] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of an IoT-based traffic monitoring system.

[0017] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. Detailed description of the invention:

[0018] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation on the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0019] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0020] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0021] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0023] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0024] Fig.1 shows a block diagram of an IoT-based traffic monitoring system comprising: a plurality of sensor units 102, each sensor unit comprising a video camera, an infrared sensor, a GPS module, and an accelerometer; a local processing unit 104 connected to each sensor unit and configured to filter and analyze raw traffic data; a central processing unit 106 configured to receive processed data from the local processing unit, further analyze the data using machine learning techniques, and optimize traffic control measures; a communication framework 108 with a wireless sensor network and a mobile ad hoc network for transmitting data between the sensor units and the central processing unit; a traffic management interface 110 that provides traffic authorities with real-time information on traffic conditions and control functions;an end-user application interface 112 for communicating real-time traffic updates to commuters and service providers;

[0025] In one embodiment, the central processing unit 106 is also configured to archive traffic data, enabling long-term analysis and evaluation of historical traffic trends as a basis for future traffic management strategies.

[0026] In one embodiment, the sensor units are configured to collect data on the number, speed, and classification of vehicles at predefined intervals for continuous traffic flow monitoring.

[0027] In one embodiment, the video cameras in the sensor units are equipped with image recognition software to detect vehicles and classify them into categories such as cars, trucks, and motorcycles.

[0028] In one embodiment, the infrared sensors are configured to monitor traffic in low visibility conditions, including at night or in inclement weather, by detecting the heat signatures of vehicles.

[0029] In one embodiment, the GPS modules are configured to track vehicle movement patterns and forward location data in real time to the central processing unit for further analysis of traffic density.

[0030] In one embodiment, the local processing unit further comprises a data filtering module configured to remove redundant or irrelevant traffic data before transmitting the filtered data to the central processing unit.

[0031] In one embodiment, the central processing unit comprises a predictive analysis module configured to predict traffic congestion based on historical data and real-time traffic patterns.

[0032] In one embodiment, the machine learning techniques embedded in the central processing unit 106 are specifically configured to dynamically adjust traffic light sequences based on the analysis of real-time traffic conditions, thereby improving the efficiency of traffic flow at intersections.

[0033] In one embodiment, the present system further includes solar-powered sensor units 114 that incorporate energy harvesting functions, thus providing improved energy efficiency and sustainability in the operation of the sensor network.

[0034] In one embodiment, the communication framework 108 uses end-to-end encryption protocols to secure the data transmission between the sensor units, the local processing units, and the central processing unit, thereby ensuring the integrity and confidentiality of the exchanged traffic data.

[0035] The IoT-based traffic monitoring system consists of several components that work together to provide comprehensive traffic management solutions. At the core of the system is a network of traffic sensors strategically placed at intersections, along roads, and near major traffic junctions. These sensors can include radar detectors, cameras, and environmental monitoring devices that can measure vehicle speed, vehicle count, and environmental conditions such as weather and pollution levels.

[0036] Each sensor is equipped with a microcontroller and a wireless communication module, enabling it to transmit real-time data to a central processing unit in a dedicated traffic management center. The processing unit is designed to aggregate incoming data from various sources, providing a holistic view of traffic conditions. Using advanced data analytics and machine learning techniques, the processing unit can analyze the data to identify trends, detect anomalies, and predict potential traffic problems, enabling proactive traffic management.

[0037] The user interface is an integral part of the system and provides traffic authorities and drivers with a graphical representation of traffic data. The user interface enables traffic managers to dynamically adjust traffic lights based on real-time data, thus improving traffic flow. In addition, the system includes a mobile application for end users that provides real-time traffic updates, route suggestions, and alerts about congestion or accidents, thus improving the overall travel experience.

[0038] The IoT-based traffic monitoring system is scalable and adaptable, allowing the integration of additional sensors or devices as needed. It can also be expanded with features such as automatic accident detection, emergency vehicle pre-emption, and integration with smart city infrastructure, contributing to improved urban mobility.

[0039] The device consists of a central processing unit housing the data analysis module, a suite of traffic sensors (including radar, cameras, and environmental monitors), a wireless communication module for data transmission, and a user interface for displaying traffic information. The system is housed in a weatherproof enclosure suitable for outdoor use, ensuring durability and reliability under diverse environmental conditions. The sensors are mounted on poles or buildings and connected to the central processing unit via a secure wireless network, enabling efficient data flow and system operation.

[0040] The device can operate autonomously or in conjunction with existing traffic management systems and offers an innovative solution for monitoring and controlling urban traffic.

[0041] The IoT-based traffic monitoring system is designed to utilize a network of sensor units and advanced data processing capabilities to improve traffic management and optimize vehicle flow. Each sensor unit in the system is equipped with multiple components, including a high-resolution video camera that captures traffic conditions in real time, an infrared sensor that detects the presence and movement of vehicles, a GPS module for precise location tracking, and an accelerometer that measures vehicle dynamics. This combination of sensors enables a comprehensive assessment of traffic patterns and provides critical data for effective decision-making.

[0042] The data collected by each sensor unit is first filtered and analyzed by a local processing unit. This unit is programmed to perform initial data processing tasks such as noise reduction and feature extraction, ensuring that only relevant traffic data is transmitted to the central processing unit. The local processing unit uses techniques to determine key traffic metrics, including vehicle counts, speeds, and movement trajectories, allowing it to respond quickly to changes in traffic conditions.

[0043] Once the data has been processed locally, it is transmitted to a central processing unit via a communication framework that includes a wireless sensor network and a mobile ad hoc network. The central processing unit is equipped with advanced machine learning techniques that enable deeper analysis of the incoming data. These techniques are designed to detect traffic patterns and predict potential congestion scenarios by evaluating various inputs, such as the historical traffic data archived in the system. The platform uses supervised learning techniques, which use historical traffic data as training inputs, to refine its predictive models, ultimately enabling it to predict peak traffic times and recommend optimal traffic control measures.

[0044] One of the key functions of machine learning techniques is to dynamically adapt traffic light sequences to real-time traffic conditions. This capability is critical for improving traffic flow at intersections, as the techniques analyze data from multiple sensor units simultaneously. By evaluating the number of vehicles waiting at traffic lights, their speeds, and the time of day, the system can optimize green phases and minimize delays, thereby reducing congestion and improving overall road safety.

[0045] In addition to its traffic management capabilities, the system features an archiving function in the central processing unit that stores traffic data for long-term analysis. This archived data is invaluable for identifying trends over time. It enables traffic authorities to identify patterns in traffic behavior and make informed decisions about infrastructure improvements and resource allocation.

[0046] The IoT-based traffic monitoring system also emphasizes energy efficiency through the integration of solar-powered sensor units. These units are equipped with energy harvesting capabilities that allow them to operate independently of traditional power sources, reducing operating costs and promoting sustainability. The energy harvesting technology ensures continuous operation even in remote or underdeveloped areas, making the system more accessible and effective in diverse environments.

[0047] Data security is a top priority in the system design. The communication framework uses end-to-end encryption protocols to protect data as it travels between sensor units, local processing units, and the central processing unit. This encryption ensures that sensitive traffic data remains protected from unauthorized access or tampering, preserves information integrity, and promotes trust between users and stakeholders.

[0048] Finally, the system's user interfaces—consisting of a traffic management interface for authorities and an end-user application for commuters—enable seamless interaction with the collected data. Traffic authorities can access real-time information, control traffic lights, and receive alerts about traffic incidents through an intuitive management interface. At the same time, the end-user application provides live traffic updates, route suggestions, and notifications about congestion or accidents, enabling commuters to make informed travel decisions.

[0049] In summary, the IoT-based traffic monitoring system integrates a variety of advanced technologies and techniques to provide a comprehensive and efficient solution for urban traffic management. Leveraging a network of sensor units, data processing, dynamic traffic light adjustments, and energy-efficient operation, the system not only overcomes the challenges of current traffic monitoring solutions but also paves the way for smarter, more sustainable urban transportation networks.

[0050] The invention relates to the technical field of intelligent transportation systems, and in particular to the development and implementation of IoT-based traffic monitoring and control solutions. It encompasses various technologies, including sensor networks, data processing techniques, machine learning, and communication protocols, aimed at improving the efficiency and effectiveness of urban traffic control systems. The invention aims to address existing limitations of conventional traffic monitoring methods by providing a comprehensive, real-time solution that integrates multiple data sources, thus enabling better decision-making for transportation authorities and improving the travel experience for commuters.

[0051] The present invention presents an IoT-based traffic monitoring system that aims to improve traffic management through real-time data collection, intelligent processing, and dynamic control. This system consists of several critical components, each of which is crucial for its effective operation. In particular, the invention is intended to enable real-time monitoring, analysis, and management of traffic conditions in urban and suburban road networks. By employing a range of sensor technologies, communication protocols, and machine learning techniques, the system collects comprehensive traffic data, processes it locally, and transmits it to a central platform for informed decision-making.The main objectives of this system include relieving traffic congestion, reducing the risk of accidents, and enabling dynamic, real-time adjustments to traffic control measures such as traffic lights and lane management to ensure smooth vehicle flow and improve overall road safety.

[0052] The system's architecture comprises several essential components, including sensor units, local processing units, a central processing unit, and a communications framework. Sensor units are strategically placed at critical traffic junctions, including roads, intersections, and high-traffic areas. These units contain video cameras, infrared sensors, GPS modules, and accelerometers. The video cameras capture high-resolution footage of traffic conditions, providing detailed insights into vehicle volume, speeds, and unusual incidents such as accidents or roadblocks. Infrared sensors enable the detection of vehicle movements even in low-light conditions, ensuring effective operation at night or in poor visibility. GPS modules continuously monitor vehicle locations, speeds, and directions, providing accurate, real-time vehicle positioning data.The accelerometers detect abrupt changes in vehicle movement, such as sudden braking or collisions, which may indicate emergencies.

[0053] The data collected by these sensor units is transmitted to nearby local processing units. These local units perform initial data processing, filter out irrelevant or noisy information, and perform basic analyses to assess traffic conditions. For example, local processing units can classify vehicles by size, calculate average traffic speeds, and assess traffic density in specific areas. Once the local analysis is complete, the processed data is sent to a central processing unit for further analysis.

[0054] In addition, the system features an intuitive traffic management interface accessible to traffic authorities, providing real-time data via a central dashboard. This dashboard visualizes the current traffic situation, including live camera feeds, vehicle counts, and congestion heat maps. Traffic authorities can use this information to make informed decisions regarding traffic rerouting, signal adjustments, or emergency response. In the event of accidents or sudden disruptions, the system can automatically alert authorities, allowing emergency services to be dispatched more quickly. The dashboard also provides access to historical data, aiding in long-term traffic analysis and infrastructure planning.

[0055] For end users, the system includes an application programming interface (API) that can be integrated with third-party applications such as GPS navigation systems and mobile apps. Commuters, delivery services, and other road users can receive real-time traffic information, route suggestions, and estimated arrival times based on processed data. The system can also inform users about road closures, traffic jams, and accidents in their vicinity, thus improving the travel experience.

[0056] Energy efficiency is a crucial aspect of the sensor units. Many are powered by solar cells to minimize environmental impact. These sensors are equipped with low-power communication protocols and intelligent sleep modes that conserve energy during periods of low traffic activity.

[0057] Security is a top priority in the system's design. All data transmissions between sensor units, local processors, and the central platform are secured using cryptographic protocols to prevent unauthorized access and tampering. End-to-end encryption is used for all data communication to ensure that confidential information such as vehicle locations and traffic patterns remain protected. Furthermore, the system incorporates backup and fault-tolerant mechanisms to prevent data loss or operational disruption in the event of outages or cyberattacks.

[0058] Another key advantage of the system is its scalability and flexibility. The modular architecture allows for easy expansion to accommodate new sensor technologies and increased traffic volumes, while seamlessly integrating with other smart city initiatives. As new IoT devices and traffic management technologies emerge, they can be integrated into the existing system without requiring a complete overhaul of the infrastructure. This adaptability ensures that the system evolves alongside the changing needs of urban environments.

[0059] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0060] Advantages, other benefits, and solutions to problems have been described above with respect to specific embodiments. The advantages, benefits, solutions to problems, and all components that can cause an advantage, benefit, or solution to occur or become more pronounced are j but should not be construed as a critical, necessary, or essential feature or component of any or all of the claims. REFERENCES 100 A block diagram of an IoT-based traffic monitoring system. 102 Variety of sensor units 104 Local processing unit 106 Central processing platform 108 Framework for Communication 110 Traffic management interface 112 End User Application Interface

Claims

[1] An IoT-based traffic monitoring system consisting of: a plurality of sensor units, each sensor unit comprising a video camera, an infrared sensor, a GPS module, and an accelerometer; a local processing unit connected to each sensor unit and configured to filter and analyze raw traffic data; a central processing unit configured to receive processed data from the local processing unit, further analyze the data using machine learning, and optimize traffic control measures, wherein the central processing unit is configured to dynamically adjust traffic light sequences based on the analysis of real-time traffic conditions, thereby improving the efficiency of traffic flow at intersections. a communication framework comprising a wireless sensor network and a mobile ad hoc network to transmit data between the sensor units and the central processing unit; a traffic management interface that provides traffic authorities with real-time traffic conditions and control functions; and an end-user application interface to provide real-time traffic information to commuters and service providers. [2] The IoT-based traffic monitoring system according to claim 1, wherein the sensor units are configured to collect data on the number, speed and classification of vehicles at predefined intervals for continuous monitoring of traffic flow. [3] The IoT-based traffic monitoring system according to claim 1, wherein the video cameras in the sensor units are equipped with image recognition software to detect vehicles and classify them into categories such as cars, trucks and motorcycles. [4] The IoT-based traffic monitoring system according to claim 1, wherein the infrared sensors are configured to monitor traffic in low visibility conditions, including at night or in bad weather, by detecting the heat signatures of vehicles. [5] The IoT-based traffic monitoring system according to claim 1, wherein the GPS modules are configured to track vehicle movement patterns and forward real-time location data to the central processing unit for further traffic density analysis. [6] The IoT-based traffic monitoring system of claim 1, wherein the local processing unit further comprises a data filtering module configured to remove redundant or irrelevant traffic data before transmitting the filtered data to the central processing unit. [7] The IoT-based traffic monitoring system according to claim 1, wherein the central processing unit comprises a predictive analysis module configured to predict traffic congestion based on historical data and real-time traffic patterns.

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