ADAPTIVE TRAFFIC SIGNAL CONTROL SYSTEM USING REAL-TIME DENSITY ANALYSIS

Abstract

Abstract The present disclosure provides a traffic control system for adjusting traffic light signals based on real-time traffic density. The system includes a camera array positioned to capture images of multiple lanes at an intersection. A microcontroller operatively connected to said camera array processes video feeds using computer vision to detect and count vehicles within each camera’s field of view. A density analysis categorizes traffic flow into predefined density levels based on vehicle count data received from said microcontroller. A traffic light controller adjusts green, yellow, and red light durations for each lane direction based on traffic density levels output from the density analysis. A communication network facilitates data transmission between the microcontroller, the density analysis, and the traffic light controller. An integration interface provides compatibility with existing traffic management infrastructure, wherein real-time signal timing adjustments optimize traffic flow at the intersection.

Specification

Description:ADAPTIVE TRAFFIC SIGNAL CONTROL SYSTEM USING REAL-TIME DENSITY ANALYSIS
Field of the Invention
[0001] The present disclosure generally relates to traffic management systems. Further, the present disclosure particularly relates to systems adjusting traffic signal durations based on real-time density analysis.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The rapid expansion of urban areas and rising vehicle density have increased traffic congestion, particularly at intersections. Conventional traffic control systems have typically relied on fixed-time signal cycles that fail to adapt to varying real-time traffic conditions. Such traditional systems often lead to inefficient traffic management, contributing to longer travel times, increased fuel consumption, and higher levels of vehicular emissions due to idling at intersections. Various attempts have been made to improve traffic flow by adjusting signal timings based on general traffic patterns; however, such approaches have remained limited in effectiveness due to their inability to dynamically respond to real-time traffic density variations.
[0004] Traffic-responsive systems have been developed to address traffic congestion by using basic sensors, such as inductive loops or infrared sensors, embedded in roadways to detect vehicle presence or count vehicles passing over designated points. However, such systems are often hindered by high installation and maintenance costs, particularly in densely populated urban environments where frequent roadwork can damage such sensors. Additionally, inductive loop sensors are restricted to single-lane detection and are typically unable to accurately assess multi-lane or turning movements, leading to suboptimal adjustments of signal timings.
[0005] Other systems utilizing pressure-sensitive sensors have attempted to provide more accurate traffic density data by measuring vehicle weight at various points within an intersection. Such systems may provide general information on vehicle presence but are often less effective in monitoring real-time traffic flow rates. These sensors are also vulnerable to mechanical wear and are frequently affected by environmental factors, further limiting accuracy and reliability. Moreover, such solutions often fail to distinguish between vehicles stopped at a red light and those in motion, leading to inconsistencies in traffic light adjustments.
[0006] More recent advancements in traffic control have incorporated video-based solutions that use cameras to monitor traffic flow. Conventional video-based systems apply image-processing techniques to capture and analyse video feeds of traffic in real-time. While such systems offer advantages over sensor-based systems by monitoring multi-lane movements, they are limited by the static nature of traditional image-processing methods that are unable to adapt to the dynamic conditions of urban intersections. Conventional image-processing techniques are further constrained by environmental factors, including weather conditions and variations in lighting, which can lead to inaccurate data capture and hinder traffic signal adjustments.
[0007] Some traffic management systems have integrated video monitoring with predictive traffic algorithms that attempt to anticipate traffic congestion by analysing historical traffic data and predefining signal adjustments based on time-of-day and average traffic flow trends. However, such systems often lack the flexibility to respond to real-time fluctuations in traffic density, as they rely on pre-configured patterns rather than live data. Consequently, such systems are ill-equipped to manage sudden increases or decreases in vehicle flow, resulting in less efficient traffic management and higher congestion levels.
[0008] Other state-of-the-art systems and techniques continue to face additional challenges. Existing methods frequently experience data transmission delays due to reliance on outdated communication networks, which limits the effectiveness of signal adjustments and prevents timely traffic flow management. Additionally, many prior systems lack compatibility with existing traffic infrastructure, complicating efforts to incorporate newer traffic control mechanisms into established networks and requiring significant modifications or replacements of older components.
[0009] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for dynamically adjusting traffic signal timings based on real-time traffic density at intersections.
Summary
[00010] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] An objective of the present disclosure is to provide a system to dynamically adjust traffic light signals based on real-time traffic density, thereby optimizing traffic flow and reducing congestion. The present disclosure provides a traffic control system comprising a camera array that captures images of multiple lanes at intersections, with each camera connected to a microcontroller that processes video feeds using computer vision to detect and count vehicles. Said system further includes a density analysis that categorizes traffic flow conditions into density levels based on vehicle count data received from said microcontroller. Based on said density levels, a traffic light controller adjusts signal durations for each lane direction, with data transmitted via a communication network implementing secure exchange protocols. An integration interface enables real-time signal timing adjustments compatible with existing infrastructure.
[00013] Further, the system provides high-resolution cameras for accurate vehicle detection under varying lighting conditions and employs computer vision to analyse historical data, thereby identifying traffic congestion patterns. Additionally, said density analysis categorizes traffic flow into predefined levels, with each level corresponding to specific timing adjustments by said traffic light controller. Moreover, a communication network includes wireless data transmission with redundancy protocols to ensure continuous operation in case of network failure. The system also includes a user interface enabling traffic authorities to monitor real-time traffic conditions and manually adjust signal timings. Additionally, continuous recalibration of said density levels adjusts to seasonal or event-based variations in traffic flow. Real-time image processing using machine learning models trained on diverse traffic scenarios further enhances detection accuracy. Data compatibility with external analytics systems allows reporting on traffic patterns, and an environmental impact component estimates reductions in emissions based on reduced idling times through optimized signal timings.
Brief Description of the Drawings
[00014] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00015] FIG. 1 illustrates a traffic control system for adjusting traffic light signals based on real-time traffic density, in accordance with the embodiments of the present disclosure.
[00016] FIG. 2 illustrates a sequential diagram of the traffic control system to adjust traffic light signals based on real-time traffic density, in accordance with the embodiments of the present disclosure.
[00017] FIG. 3 illustrates a traffic control system utilizing camera-based automated and manual control to optimize signal timings, in accordance with the embodiments of the present disclosure.
Detailed Description
[00018] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00019] The use of the terms "a" and "an" and "the" and "at least one" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term "at least one" followed by a list of one or more items (for example, "at least one of A and B") is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00020] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00021] As used herein, the term "traffic control system" refers to a system that adjusts traffic light signals based on real-time traffic density at an intersection to manage the flow of vehicles across multiple lanes. Said system encompasses various interconnected components and subsystems that function collaboratively to capture traffic data, analyse such data, and execute traffic signal adjustments. Said system aims to optimise traffic flow by dynamically allocating signal durations according to traffic density. The term also applies to systems that facilitate the real-time management of traffic at intersections with complex traffic patterns, including those with high volumes of vehicles and multiple directional flows. Furthermore, such systems may include various types of sensors, processing units, and communication networks that continuously operate to monitor, process, and respond to changing traffic conditions. As used herein, such a system may include features for adapting to both high-density and low-density traffic environments, with potential integration into urban traffic infrastructure for enhanced traffic management.
[00022] As used herein, the term "camera array" refers to an arrangement of multiple cameras positioned strategically to capture images of traffic conditions in multiple lanes at an intersection. Each camera in said array is positioned to ensure comprehensive visual coverage of all lanes and traffic flows at the monitored intersection. Such cameras are typically placed at angles and heights that maximize the field of view and minimize blind spots, thereby enabling accurate observation of vehicle movement, lane occupancy, and overall traffic density. Said array may include high-resolution cameras capable of operating in various lighting conditions, including low light, to ensure consistent and accurate data collection. Additionally, the term may encompass various configurations of camera placement that allow monitoring of different types of intersections, such as roundabouts and multi-lane crossings. Furthermore, said array may include built-in components or be operatively connected to external devices to transmit captured images to a processing unit for further analysis.
[00023] As used herein, the term "microcontroller" refers to an electronic processing unit operatively connected to the camera array, functioning to receive and process video feeds captured by each camera within said array. Said microcontroller interprets and analyses such video data using computer vision to identify and count vehicles within the field of view of each camera. Additionally, said microcontroller employs processing techniques to distinguish between stationary and moving vehicles, thereby providing accurate, real-time data for further density analysis. Said processing unit is capable of managing multiple video feeds simultaneously and is structured to perform under continuous operational conditions typically present in urban traffic management systems. Furthermore, said microcontroller may be capable of interfacing with external data storage systems for historical data analysis or with communication networks to relay processed information to other components in the traffic control system.
[00024] As used herein, the term "density analysis" refers to a component of the traffic control system responsible for categorising traffic flow conditions into specific density levels based on vehicle count data processed by the microcontroller. Said density analysis assesses traffic patterns at an intersection by utilising data on the number of vehicles and their positioning within each lane. Said analysis identifies traffic conditions, which may include low, medium, and high-density levels, each representing a distinct level of vehicle concentration. Such categorisation informs subsequent signal adjustments by indicating the most efficient light durations for current traffic conditions. Said density analysis may further include processing mechanisms that consider environmental factors or periodic variations in traffic volume, allowing said system to remain adaptive across different times of day or seasons. Additionally, such density analysis allows for dynamic recalibration, ensuring accuracy in diverse traffic conditions.
[00025] As used herein, the term "traffic light controller" refers to a subsystem of the traffic control system that regulates the timing of green, yellow, and red light durations for each lane direction based on density levels determined by said density analysis. Said traffic light controller receives data inputs regarding current traffic conditions and adjusts signal durations to optimize the flow of vehicles through the intersection. Such adjustments allow prioritisation of lanes or directions with higher vehicle density, reducing congestion and promoting a balanced flow of traffic. Said controller includes an internal mechanism for real-time signal changes that respond promptly to density updates provided by other system components. Additionally, the traffic light controller may offer manual override capabilities for use by traffic authorities during emergencies or abnormal traffic events, thus maintaining flexibility in traffic management.
[00026] As used herein, the term "communication network" refers to a data transmission infrastructure that facilitates real-time communication between the microcontroller, density analysis, and traffic light controller within the traffic control system. Said network enables secure and efficient exchange of data necessary for accurate traffic density assessment and timely signal adjustments. Said communication network may support both wired and wireless transmission modes, with encryption protocols to protect the integrity of transmitted data. Additionally, said network is structured to handle high data volumes without delay, ensuring that signal adjustments are executed in a timely manner as traffic density fluctuates. In some configurations, redundancy protocols may be included within said network to maintain data transmission continuity even during network disruptions, thereby supporting the reliable operation of the traffic control system under varied conditions.
[00027] As used herein, the term "integration interface" refers to a system component that facilitates compatibility between the traffic control system and existing traffic management infrastructure. Said interface enables seamless integration of the traffic control system into urban traffic environments, allowing said system to function alongside pre-existing traffic management setups. Said interface allows for flexible configuration and calibration to ensure accurate alignment with other intersection management protocols already in use. Additionally, said interface may provide a means for data exchange between the traffic control system and broader urban management networks or databases, thereby enhancing overall traffic management capabilities within an interconnected urban framework. Said integration further enables the traffic control system to implement real-time signal adjustments, enhancing the efficiency and responsiveness of existing infrastructure in managing dynamic traffic patterns.
[00028] FIG. 1 illustrates a traffic control system for adjusting traffic light signals based on real-time traffic density, in accordance with the embodiments of the present disclosure. In an embodiment, a camera array is positioned to capture images of multiple lanes at an intersection. Said camera array is arranged such that each camera is strategically positioned to cover the full breadth and depth of each lane within the intersection to ensure that all vehicles passing through each lane are adequately observed. Each camera within said array may be oriented to achieve an optimal field of view that includes both incoming and outgoing traffic flows, as well as turning lanes where applicable. Additionally, each camera may include high-resolution imaging capabilities to ensure that image clarity remains sufficient for detailed analysis, regardless of traffic density or vehicle speed. Further, each camera in said array may feature low-light or night vision capabilities to maintain image quality during nighttime conditions or in environments with limited lighting. Said camera array is connected to a mounting structure, allowing each camera to be securely positioned and protected from environmental factors such as wind, rain, or dust. In certain configurations, each camera may include internal or external housings that offer weatherproofing and anti-glare components to maintain the quality of captured images. Said array may also operate under a synchronized timing mechanism, capturing images or video streams at pre-set intervals or in real-time to ensure continuity in traffic monitoring. The captured images are then transmitted to a processing unit for further analysis, which in certain embodiments may include preliminary data processing capabilities within the camera system itself to enhance operational efficiency.
[00029] In an embodiment, a microcontroller is operatively connected to the camera array and processes video feeds using computer vision to detect and count vehicles within the field of view of each camera. Said microcontroller receives continuous video streams or periodically captured images from each camera in the array, which are subsequently processed using data processing methods to identify and isolate individual vehicles. In certain configurations, said microcontroller may employ image segmentation to separate the vehicles from the background environment, allowing for accurate detection regardless of environmental conditions. The microcontroller is structured to handle high data processing loads, enabling simultaneous analysis of multiple video feeds in real-time. Computer vision techniques applied by the microcontroller may include object recognition, edge detection, and motion tracking, allowing the system to determine the exact number of vehicles within each lane. Said microcontroller may also differentiate between stationary and moving vehicles, providing additional traffic flow information that may inform further system adjustments. In certain configurations, the microcontroller may store temporarily processed data to identify recurring patterns, which may be used for subsequent predictive traffic analyses. In another configuration, the microcontroller may adjust processing rates based on real-time traffic density, thus optimising data throughput and ensuring timely responses.
[00030] In an embodiment, a density analysis categorises traffic flow conditions into predefined density levels based on vehicle count data

received from the microcontroller. Said density analysis assesses the volume of vehicles detected within each lane and classifies said volume into specific density levels, such as low, medium, or high. Each density level corresponds to a range of vehicle counts and may be applied to adjust signal timings accordingly. Density categorisation may also consider additional factors, such as average vehicle speed, vehicle type, and lane occupancy percentage, to refine the density assessment. In certain configurations, density analysis may further involve calculating average waiting times for vehicles within each density level, providing a comprehensive overview of traffic conditions. Such a categorisation is performed in real-time to allow dynamic adjustments to traffic light signals. Said density analysis may include data filters to mitigate the effect of anomalies, such as short-term spikes in vehicle count, which may arise from non-standard traffic events. In other configurations, density levels may be updated periodically or based on seasonally adjusted traffic trends, allowing adaptability across different times and situations.
[00031] In an embodiment, a traffic light controller adjusts green, yellow, and red light durations for each lane direction based on density levels determined by the density analysis. Said controller receives density data outputs, categorising current traffic conditions into predefined density levels and applying specific timing adjustments corresponding to said levels. The traffic light controller comprises a control circuit, enabling the immediate alteration of signal durations across multiple lanes. Each signal duration is adjusted to prioritize the flow of high-density lanes while maintaining acceptable wait times for lower-density lanes. In certain configurations, said traffic light controller may implement variable timing strategies, allocating additional green light time to lanes with persistently high-density levels, thereby minimising congestion. Furthermore, the traffic light controller may include an interface for manual input by traffic management personnel to override automated timings when necessary, such as during emergency situations or roadwork. The traffic light controller operates on a continuous cycle, receiving density data updates at regular intervals, ensuring that signal durations remain adaptive and responsive to real-time traffic conditions.
[00032] In an embodiment, a communication network facilitates data transmission between the microcontroller, density analysis, and traffic light controller. Said communication network transmits data such as vehicle counts, density classifications, and signal duration adjustments to ensure a coordinated and responsive traffic control system. Said network may include wired or wireless communication methods, depending on infrastructure requirements, and is structured to handle high volumes of data with minimal latency. Each component within the communication network is capable of securely transmitting data to prevent interference or unauthorized access. Redundancy features may be incorporated into said network to ensure data integrity and operational continuity during temporary network disruptions. In certain configurations, encryption methods are applied to safeguard transmitted data, particularly in urban environments where multiple networks may coexist. Said network's capabilities enable real-time interactions between the microcontroller, density analysis, and traffic light controller, allowing timely adjustments to traffic signals based on current traffic flow conditions.
[00033] In an embodiment, an integration interface enables compatibility between the traffic control system and existing traffic management infrastructure. Said interface provides interoperability with legacy systems, allowing the traffic control system to function cohesively with pre-installed traffic management setups at intersections. The integration interface may facilitate data exchange between the traffic control system and existing urban traffic networks, allowing system operators to utilise existing infrastructure without requiring extensive modifications. In certain configurations, the integration interface may support both hardware and software components, ensuring that both physical and digital connections are compatible with a wide range of traffic management frameworks. Additionally, said interface may allow real-time calibration of system settings to align with predefined urban traffic management parameters, such as signal timing presets or priority lane allocations. The integration interface may also support expansion capabilities, enabling system adaptability as urban traffic requirements evolve over time. In specific configurations, said interface is structured to facilitate communication with external databases or analytical platforms for additional data insights into traffic patterns and intersection performance.
[00034] In an embodiment, the camera array includes high-resolution cameras with low-light capabilities to ensure accurate vehicle detection in various lighting conditions. Said cameras are equipped with advanced imaging sensors that capture clear and detailed images even during low-light conditions, such as nighttime or during inclement weather when natural visibility is reduced. Such cameras may also incorporate technology that minimizes motion blur, providing precise vehicle capture in cases of high-speed traffic. Each camera in the array is positioned strategically to maximize field coverage across all lanes of the intersection, reducing the risk of blind spots. The high-resolution feature allows the cameras to capture detailed images, which are essential for accurately identifying and counting vehicles at different times of the day and in different lighting scenarios. Low-light capabilities are further enhanced by additional components, such as infrared sensors or adaptive exposure settings, allowing consistent data quality regardless of environmental lighting changes. Said cameras may also be equipped with protective housings to shield sensitive imaging equipment from adverse weather conditions, thereby preserving the integrity and functionality of the array. The camera array, with its high-resolution and low-light features, provides essential real-time visual data to the traffic control system, which is necessary for subsequent analysis and processing stages within the system.
[00035] In an embodiment, computer vision techniques applied by the microcontroller include processes for identifying traffic congestion patterns by analysing historical traffic data to predict high-density periods. Said computer vision processes involve the storage of historical data on traffic volume, flow rates, and vehicle counts, which is then used to identify recurring congestion trends. By assessing patterns over time, the system can establish predictive indicators that suggest likely high-density periods, such as rush hours or weekends, allowing pre-emptive adjustments to traffic signal timings. The computer vision processes may also include data clustering methods, categorizing traffic trends based on factors such as time of day, weather conditions, and seasonal variations. Such predictions are based on a cumulative analysis of past traffic data, which provides an empirical basis for the system to anticipate and manage congestion. Additionally, predictive data may be compared with real-time vehicle counts, enabling an adaptive response that refines accuracy over time. The predictive processes in said system provide valuable insights into typical congestion patterns, making it possible to anticipate and respond to high-density periods efficiently.
[00036] In an embodiment, the density analysis categorises traffic flow conditions into predefined levels of low, medium, and high density, each associated with specific timing adjustments for the traffic light controller. Said categorisation is based on real-time data received from the microcontroller, which counts vehicles within each lane at the intersection. Each density level is defined by a predetermined vehicle count range, allowing the system to dynamically assess traffic volume. Low-density categorisation applies when vehicle counts are minimal, indicating a smooth flow of traffic that requires standard signal timing. Medium-density categorisation represents moderate vehicle counts that may warrant minor timing adjustments to optimize traffic flow. High-density categorisation signifies significant vehicle accumulation, necessitating extended green light durations to alleviate congestion in specific lanes. The density analysis may adjust density thresholds periodically to reflect changes in traffic patterns based on location, time of day, or seasonal variations. Such categorisation, conducted in real time, enables an adaptive response to varying traffic conditions by providing tailored timing adjustments for each density level.
[00037] In an embodiment, the communication network within the traffic control system operates using wireless data transmission methods, with redundancy measures to maintain continuous operation during network disruptions. Said network transmits data between key components, such as the microcontroller, density analysis, and traffic light controller, ensuring coordinated traffic management. The wireless nature of said network eliminates the need for extensive physical infrastructure, which can be especially beneficial in urban environments. Each component within the network transmits and receives data in real time, allowing for immediate adjustments in response to changing traffic density. Redundancy measures incorporated within the network include backup transmission channels or alternative wireless frequencies, which activate automatically if primary channels experience failure. Such redundancy protocols ensure that the traffic control system remains operational and maintains accurate data flow, even during instances of temporary network instability. The communication network's structure supports high-frequency data transmission without delay, promoting efficient and uninterrupted operation of the traffic management processes.
[00038] In an embodiment, the traffic light controller includes a user interface allowing traffic management authorities to monitor real-time traffic conditions and manually override signal timings when necessary. Said user interface displays live data on vehicle counts, density levels, and current signal timings for each lane, providing authorities with a comprehensive view of traffic at the monitored intersection. The interface may include a touch-screen or control panel that enables authorized personnel to manually adjust signal durations or activate emergency protocols when required. Said interface also includes alert mechanisms that notify operators of unusual traffic patterns, such as sudden congestion or lane blockages, enabling quick intervention. Additionally, the user interface allows traffic authorities to configure timing presets for specific situations, such as peak traffic hours or events, thus maintaining optimal traffic flow. The integration of a user interface provides manual control, offering flexibility for authorities in managing dynamic or emergency traffic scenarios.
[00039] In an embodiment, the density analysis continually recalibrates predefined density levels based on seasonal or event-based variations in traffic volume, thereby maintaining accurate categorisation. Said recalibration process involves periodic updates to the thresholds that define low, medium, and high-density levels, based on data trends observed over time. For instance, during peak travel seasons or regional events, the system may lower the vehicle count threshold for high-density classification to account for increased traffic volume. The recalibration process may incorporate predictive data from historical records, adjusting density levels in anticipation of recurring patterns. Seasonal adjustments ensure that signal timing responses remain proportional to real-time traffic demands, accommodating shifts in traffic flow without requiring manual input. Event-based recalibrations are similarly applied, modifying density levels in response to temporary traffic disruptions, such as road construction or detours. Continuous recalibration supports adaptability in traffic management across various conditions, facilitating optimal traffic flow at intersections.
[00040] In an embodiment, the microcontroller performs real-time image processing using machine learning models trained on diverse traffic scenarios to enhance vehicle detection accuracy. Said machine learning models have been trained with data from various traffic conditions, including different times of day, seasons, and environmental factors, allowing the microcontroller to recognize and categorize vehicles under a wide range of circumstances. Such real-time image processing involves distinguishing between vehicle types, lane positions, and movement patterns, enabling precise and responsive traffic flow analysis. By utilizing machine learning, the microcontroller can also recognize anomalies, such as stopped vehicles or unexpected obstacles, that may impact traffic density. The adaptive capabilities of the machine learning models improve detection accuracy over time as additional data is incorporated, further refining the system's responsiveness to varying traffic conditions. This continuous learning process enhances the system's overall performance in managing traffic at complex intersections.
[00041] In an embodiment, the integration interface allows compatibility between the traffic control system and external data analytics systems for generating reports on traffic patterns and intersection performance. Said interface enables seamless data exchange between the traffic control system and urban traffic management platforms, which may include databases, analysis tools, or monitoring applications. The interface allows external systems to access data on traffic flow, vehicle counts, and signal adjustments, providing insights into intersection performance and long-term traffic trends. Said integration interface also supports interoperability with other urban infrastructure components, facilitating coordinated traffic management across multiple intersections. In certain configurations, the interface allows authorities to retrieve historical data or run diagnostic analyses on traffic patterns, identifying areas for improvement in traffic management strategies. The integration interface thus extends the utility of the traffic control system by enabling comprehensive traffic analysis through external data platforms.
[00042] In an embodiment, an environmental impact component calculates estimated reductions in vehicle emissions based on reduced idling times associated with optimized traffic signal timings. Said component is operatively connected to the density analysis, using data on vehicle count and timing adjustments to estimate changes in fuel consumption and emissions levels. Such environmental assessments factor in the duration of signal timings for each density level, calculating potential reductions in idling time and emissions output. The component may include additional metrics, such as average vehicle speed or lane occupancy rates, to refine emission estimates under varying traffic conditions. The calculations generated by said environmental impact component may be stored or transmitted to external databases, allowing long-term monitoring of emissions trends across intersections.
[00043] FIG. 2 illustrates a sequential diagram of the traffic control system to adjust traffic light signals based on real-time traffic density, in accordance with the embodiments of the present disclosure. The process begins with the camera array capturing images of multiple lanes at an intersection, relaying these images to the microcontroller. The microcontroller processes the video feeds, detecting and counting vehicles within each lane's field of view. This vehicle count data is then sent to the density analysis component, which categorizes traffic flow into specific density levels (e.g., low, medium, high) based on the received data. Following this categorization, the density analysis transmits the traffic density level to the traffic light controller, which adjusts the green, yellow, and red signal timings accordingly to optimize flow. The updated signal timings are communicated through the communication network, which enables data exchange between components. Finally, the integration interface ensures that these adjustments are compatible with existing traffic infrastructure, allowing for real-time signal optimization across the intersection.
[00044] FIG. 3 illustrates a traffic control system utilizing camera-based automated and manual control to optimize signal timings, in accordance with the embodiments of the present disclosure. The illustration depicts a traffic control system utilizing camera-based automated and manual control to optimize signal timings. Initially, the camera installation captures real-time traffic images, which are then processed through a central automated control system. This system integrates computer vision and machine learning to analyze the images, enabling vehicle detection and traffic density estimation. Additionally, the system allows for manual control using RF modules, offering traffic authorities the flexibility to override automated settings if necessary. Based on the processed data, the automated control adjusts the traffic light signals by sending commands to the signal control unit. This unit regulates the timing and color of the lights, as well as the countdown display visible to approaching vehicles, ensuring efficient traffic management. The combined use of automated and manual control methods provides a robust solution to dynamically manage traffic flow and minimize congestion at intersections.
[00045] In an embodiment, a traffic control system for adjusting traffic light signals based on real-time traffic density provides a dynamic response to varying traffic levels, enhancing flow efficiency and reducing congestion at intersections. A camera array positioned to capture images of multiple lanes continuously monitors traffic, delivering real-time visual data. The microcontroller processes video feeds using computer vision to detect and count vehicles within the field of view, enabling the system to gauge traffic density accurately. The density analysis categorizes traffic flow into predefined levels based on vehicle count data received from the microcontroller, guiding the traffic light controller in adjusting green, yellow, and red light durations. The communication network facilitates seamless data transmission between the microcontroller, density analysis, and traffic light controller, enabling real-time responsiveness. An integration interface allows compatibility with existing infrastructure, permitting smooth adoption of said traffic control system into established urban settings.
[00046] In an embodiment, said camera array includes high-resolution cameras with low-light capabilities to maintain accurate vehicle detection under variable lighting conditions. High-resolution imaging captures detailed visual data, ensuring accurate vehicle detection across different lane positions. The low-light capability enables effective detection during nighttime or adverse weather conditions when natural visibility is limited. This feature is critical for uninterrupted traffic monitoring, allowing the system to function effectively around the clock. The camera array's advanced imaging capacity reduces the risk of missed detections or inaccurate vehicle counts that may otherwise occur due to low visibility. Additionally, high-resolution images support precise vehicle classification and tracking, which further informs the microcontroller's processing accuracy. The array's enhanced visual capture capacity directly supports the system's responsiveness and accuracy in adjusting traffic signals based on real-time traffic volume.
[00047] In an embodiment, said computer vision processes identify traffic congestion patterns by analyzing historical data to predict high-density periods, allowing proactive adjustments to traffic signals. By evaluating recurring traffic trends such as peak hours, weather patterns, or event-related congestion, the system can anticipate periods of high density and adjust signal timings accordingly. This predictive capability minimizes delays by pre-emptively allocating longer green-light durations to lanes with expected high-density levels. Historical data analysis also allows the system to distinguish between regular traffic fluctuations and potential anomalies, reducing the likelihood of errors in signal adjustment. The ability to predict high-density periods provides a pre-emptive response to congestion, thereby reducing stop-and-go traffic that typically leads to increased fuel consumption and emissions. Integrating historical data into the computer vision processes enhances overall traffic management effectiveness by supporting smoother, more efficient traffic flow.
[00048] In an embodiment, the density analysis categorizes traffic flow into low, medium, and high-density levels, each corresponding to predefined timing adjustments for the traffic light controller. Said categorization offers a responsive approach to traffic management, adapting signal timings to real-time traffic needs. Low-density levels initiate standard timing cycles, while medium-density levels may slightly increase green light durations for certain lanes. High-density levels prioritize congestion relief, allocating longer green light durations to heavily used lanes and enabling smoother traffic flow. By defining specific timing responses for each density level, the system addresses varying traffic volumes more effectively. The density levels provide a framework that simplifies timing adjustments, allowing traffic light signals to operate in alignment with real-time conditions. Such categorization also minimizes the likelihood of extended red-light waiting times, thereby improving travel efficiency and reducing delays at intersections.
[00049] In an embodiment, the communication network supports wireless data transmission with redundancy measures to ensure continuous operation during potential network disruptions. The wireless design eliminates the need for extensive physical wiring, allowing flexibility in deployment across urban areas. Redundancy protocols, including backup channels or alternative transmission pathways, automatically activate if the primary transmission experiences interruptions, maintaining data flow without manual intervention. This redundancy is essential for the system's real-time performance, as signal adjustments based on traffic density require uninterrupted data transmission between the microcontroller, density analysis, and traffic light controller. By securing reliable data transmission, the communication network enables consistent updates to traffic signal timing, even in areas with variable network stability. Wireless transmission further reduces installation complexity, making the network adaptable to various intersection configurations without the constraints of a wired setup.
[00050] In an embodiment, the traffic light controller includes a user interface that enables traffic management authorities to monitor real-time traffic conditions and manually override signal timings when necessary. The user interface displays live data on traffic volume, density levels, and current signal durations, providing authorities with a comprehensive view of intersection conditions. A manual override option allows authorities to intervene directly in unusual or emergency situations, such as accidents, road closures, or high-priority vehicle passage. The interface may be structured to include alerts for abnormal traffic patterns, enabling prompt responses to changing conditions. This interactive capability empowers traffic authorities to exercise additional control over intersection signals, enhancing the flexibility and reliability of the system in dynamic environments. The ability to monitor and override signal timings ensures that intersection management remains responsive and adaptable to unexpected scenarios.
[00051] In an embodiment, the density analysis recalibrates predefined density levels continuously, considering seasonal or event-based variations in traffic volume. This recalibration allows the system to adjust thresholds for low, medium, and high-density categories, optimizing timing responses for different times of the year or special events. For example, during holiday seasons or major public events, the threshold for high-density categorization may be lowered to account for increased traffic flow. This continuous recalibration ensures that timing adjustments remain proportionate to actual traffic demands, thereby preventing overextended green lights or insufficient red-light durations. By adapting to seasonal and event-based fluctuations, the density analysis maintains efficient signal timings throughout diverse conditions, ensuring effective traffic management without requiring manual reconfiguration.
[00052] In an embodiment, the microcontroller performs real-time image processing using machine learning models trained on diverse traffic scenarios, enhancing vehicle detection accuracy across varying conditions. By processing live video data, the microcontroller differentiates between vehicle types, lane positions, and movement patterns, allowing for a nuanced traffic density analysis. Machine learning models improve detection capabilities by continuously adapting to complex traffic scenarios, such as high-speed movement, dense vehicle clusters, or unusual objects on the road. Real-time image processing enables immediate analysis, ensuring that signal adjustments align with current traffic conditions. The machine learning models are trained on diverse scenarios, including daytime, nighttime, and varying weather, enhancing the system's adaptability to all environmental contexts. Continuous learning from real-world traffic data allows the microcontroller to improve vehicle identification accuracy, optimizing the entire traffic control system's responsiveness to dynamic conditions.
[00053] In an embodiment, the integration interface is compatible with external data analytics systems, supporting data exchange for generating reports on traffic patterns and intersection performance. By interfacing with data analytics platforms, the system provides access to detailed traffic data, allowing authorities to analyze patterns over time. The integration interface supports data retrieval on variables such as vehicle counts, density fluctuations, and signal duration records, enabling insights into long-term intersection performance. Compatibility with external analytics systems facilitates coordinated traffic management strategies across multiple intersections, contributing to comprehensive urban traffic flow analysis. Additionally, such data can support future infrastructure planning by identifying trends that may require lane expansions, timing adjustments, or other traffic management improvements. This integration extends the utility of the traffic control system, providing a foundation for ongoing intersection optimization and city-wide traffic studies.
[00054] In an embodiment, an environmental impact component calculates reductions in vehicle emissions based on reduced idling times resulting from optimized traffic signal timings. By connecting with the density analysis, this component evaluates idling reductions associated with real-time signal adjustments, estimating associated emission decreases. Such calculations factor in vehicle types, average idling times per density level, and fuel consumption rates, generating quantitative insights into environmental benefits. Emission estimates may also consider variables like traffic speed and lane occupancy to produce comprehensive impact assessments. Reduced emissions from minimized idling contribute to improved air quality, particularly in densely populated urban areas. Emission data produced by the environmental impact component may also be integrated into larger environmental monitoring frameworks, providing city planners and traffic authorities with valuable data for developing sustainable traffic management practices.
[00055] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00056] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00057] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.












I/We Claim:
1. A traffic control system for adjusting traffic light signals based on real-time traffic density, comprising:
a camera array positioned to capture images of multiple lanes at an intersection;
a microcontroller operatively connected to said camera array, configured to process video feeds using computer vision algorithms to detect and count vehicles within the field of view of each camera in said camera array;
a density analysis module configured to categorize traffic flow conditions into predefined density levels based on vehicle count data received from said microcontroller;
a traffic light controller configured to adjust green, yellow, and red light durations for each lane direction based on traffic density levels output by said density analysis module;
a communication network facilitating data transmission between said microcontroller, said density analysis module, and said traffic light controller, wherein secure data exchange protocols are implemented;
an integration interface configured to enable compatibility with existing traffic management infrastructure, wherein adjustments to signal timings are effected in real time to optimize traffic flow at said intersection.
2. The traffic control system of claim 1, wherein said camera array includes high-resolution cameras with low-light capabilities to maintain accurate vehicle detection in varying light conditions.
3. The traffic control system of claim 1, wherein said computer vision algorithms are further configured to identify traffic congestion patterns by analyzing historical traffic data to predict high-density periods.
4. The traffic control system of claim 1, wherein said density analysis module categorizes traffic flow conditions into low, medium, and high density levels, with each level corresponding to predefined timing adjustments for said traffic light controller.
5. The traffic control system of claim 1, wherein said communication network is configured for wireless data transmission, with redundancy protocols in place to ensure continuous operation in the event of a network failure.
6. The traffic control system of claim 1, wherein said traffic light controller further comprises a user interface that allows traffic management authorities to monitor real-time traffic conditions and manually override signal timings as required.
7. The traffic control system of claim 1, wherein said density analysis module continuously recalibrates said predefined density levels based on seasonal or event-based variations in traffic volume.
8. The traffic control system of claim 1, wherein said microcontroller is configured to perform real-time image processing using machine learning models, wherein said machine learning models are trained on diverse traffic scenarios to enhance vehicle detection accuracy.
9. The traffic control system of claim 1, wherein said integration interface is further configured to provide data compatibility with external data analytics systems for the purpose of generating reports on traffic patterns and intersection performance.
10. The traffic control system of claim 1, wherein said system comprises an environmental impact module operatively connected to said density analysis module, wherein said environmental impact module calculates an estimated reduction in vehicle emissions based on reduced idling times associated with optimized traffic signal timings.



ADAPTIVE TRAFFIC SIGNAL CONTROL SYSTEM USING REAL-TIME DENSITY ANALYSIS
Abstract
The present disclosure provides a traffic control system for adjusting traffic light signals based on real-time traffic density. The system includes a camera array positioned to capture images of multiple lanes at an intersection. A microcontroller operatively connected to said camera array processes video feeds using computer vision to detect and count vehicles within each camera's field of view. A density analysis categorizes traffic flow into predefined density levels based on vehicle count data received from said microcontroller. A traffic light controller adjusts green, yellow, and red light durations for each lane direction based on traffic density levels output from the density analysis. A communication network facilitates data transmission between the microcontroller, the density analysis, and the traffic light controller. An integration interface provides compatibility with existing traffic management infrastructure, wherein real-time signal timing adjustments optimize traffic flow at the intersection.

, Claims:I/We Claim:
1. A traffic control system for adjusting traffic light signals based on real-time traffic density, comprising:
a camera array positioned to capture images of multiple lanes at an intersection;
a microcontroller operatively connected to said camera array, configured to process video feeds using computer vision algorithms to detect and count vehicles within the field of view of each camera in said camera array;
a density analysis module configured to categorize traffic flow conditions into predefined density levels based on vehicle count data received from said microcontroller;
a traffic light controller configured to adjust green, yellow, and red light durations for each lane direction based on traffic density levels output by said density analysis module;
a communication network facilitating data transmission between said microcontroller, said density analysis module, and said traffic light controller, wherein secure data exchange protocols are implemented;
an integration interface configured to enable compatibility with existing traffic management infrastructure, wherein adjustments to signal timings are effected in real time to optimize traffic flow at said intersection.
2. The traffic control system of claim 1, wherein said camera array includes high-resolution cameras with low-light capabilities to maintain accurate vehicle detection in varying light conditions.
3. The traffic control system of claim 1, wherein said computer vision algorithms are further configured to identify traffic congestion patterns by analyzing historical traffic data to predict high-density periods.
4. The traffic control system of claim 1, wherein said density analysis module categorizes traffic flow conditions into low, medium, and high density levels, with each level corresponding to predefined timing adjustments for said traffic light controller.
5. The traffic control system of claim 1, wherein said communication network is configured for wireless data transmission, with redundancy protocols in place to ensure continuous operation in the event of a network failure.
6. The traffic control system of claim 1, wherein said traffic light controller further comprises a user interface that allows traffic management authorities to monitor real-time traffic conditions and manually override signal timings as required.
7. The traffic control system of claim 1, wherein said density analysis module continuously recalibrates said predefined density levels based on seasonal or event-based variations in traffic volume.
8. The traffic control system of claim 1, wherein said microcontroller is configured to perform real-time image processing using machine learning models, wherein said machine learning models are trained on diverse traffic scenarios to enhance vehicle detection accuracy.
9. The traffic control system of claim 1, wherein said integration interface is further configured to provide data compatibility with external data analytics systems for the purpose of generating reports on traffic patterns and intersection performance.
10. The traffic control system of claim 1, wherein said system comprises an environmental impact module operatively connected to said density analysis module, wherein said environmental impact module calculates an estimated reduction in vehicle emissions based on reduced idling times associated with optimized traffic signal timings.

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