Description:Technical Field:
The present invention operates within the technical field of traffic management systems, focusing on the development and implementation of an Advanced Traffic Management System (ATMS). This innovative system encompasses a range of technologies and algorithms to address the challenges associated with conventional traffic management and enhance road safety, optimize traffic flow, and provide real-time information to users.
Background:
Traditional traffic management systems lack the sophistication required to adapt to dynamic traffic conditions and provide real-time information to users. The ATMS addresses these shortcomings by incorporating advanced technologies and subsystems to enhance road safety, traffic flow, and user experience. In the domain of traffic management, traditional systems have struggled to adapt to the complexities of modern road networks. Conventional traffic management systems often lack the agility and intelligence required to address dynamic and unpredictable traffic conditions. Many existing solutions rely on outdated technologies, limited data processing capabilities, and a lack of integration between subsystems. These limitations result in suboptimal traffic flow, compromised road safety, and an inability to provide real-time information to users. Among the common challenges faced by existing traffic management systems, a key concern is limited real-time data processing. Traditional systems encounter difficulties in efficiently processing real-time data, impeding their ability to furnish up-to-the-minute information to drivers and authorities, thus hindering effective incident management. Furthermore, inadequate incident detection contributes to delayed responses and worsens traffic disruptions, as existing systems may lack the precision required to promptly identify and assess events such as accidents, congestion, or road debris.
The patent addresses the issue of fragmented subsystems prevalent in many current traffic management systems. Operating as a collection of disparate components with minimal integration, these systems hinder the seamless exchange of data. Additionally, the patent focuses on the static nature of traditional traffic control mechanisms, which often rely on fixed speed limits and messages displayed on Variable Message Signs (VMS) without adapting to real-time traffic conditions. This lack of adaptability can result in inefficient traffic flow and contribute to congestion.
Another significant challenge is the insufficient integration of weather data into existing systems. Many traditional traffic management solutions may not adequately incorporate meteorological information, leading to a failure to anticipate and address the impact of adverse weather conditions on road safety and traffic flow. In summary, the patent proposes an Advanced Traffic Management System (ATMS) designed to overcome these limitations by incorporating advanced technologies and subsystems, aiming to enhance road safety, improve traffic flow, and elevate the overall user experience.
Summary
The Advanced Traffic Management System (ATMS) described in this patent encompasses a sophisticated network of high-resolution Traffic Monitor Camera Systems (TMCS) strategically positioned along roadways, incorporating Video Incident Detection Systems (VIDS) employing computer vision algorithms for real-time incident analysis. The system dynamically adjusts speed limits through Vehicle Actuated Speed Detection (VASD) based on traffic density and road conditions and provides real-time driver advisories via the Advanced Driver Advisory System (ADAS), seamlessly integrated with an Emergency Calling System (ECS) for enhanced safety during emergencies. Motion Detection Systems (MDS) ensure proactive safety measures by detecting pedestrian and cyclist motion, while a Central Dashboard System aggregates data for real-time analytics, incident response coordination, and dynamic traffic management. The incorporation of a Geographical Information System (GIS) enhances spatial data analysis for accurate traffic predictions, and an Automatic Traffic Classification and Counter (ATCC) ensures precise vehicle classification and traffic flow monitoring. Additionally, Weather Monitoring integrates meteorological data to anticipate and mitigate the impact of adverse weather conditions on traffic, providing dynamic updates through Variable Message Sign (VMS) boards and ADAS for optimal traffic management. The Emergency Calling System (ECS) enables users to initiate emergency calls directly from their vehicles, transmitting location-based data to emergency services for rapid response. This present invention provides an integrated solution for efficient and adaptive traffic management, leveraging advanced technologies across various subsystems.
The present invention aims to overcome the limitations of existing traffic management systems by introducing an Advanced Traffic Management System (ATMS) designed to:
Enhance Road Safety: The ATMS employs advanced technologies and algorithms to enhance incident detection and response, thereby contributing to improved road safety.
Optimize Traffic Flow: By dynamically adjusting speed limits, providing personalized driver advisories, and optimizing routing, the ATMS aims to streamline traffic flow, reducing congestion and enhancing overall road efficiency.
Provide Real-Time Information: The ATMS integrates subsystems to deliver real-time information to drivers through Variable Message Signs (VMS) and personalized advisories, improving user awareness and experience.
Ensure System Integration: The invention emphasizes the integration of subsystems, ensuring seamless communication and data exchange among components for complete traffic management.
Adapt to Dynamic Conditions: Utilizing advanced algorithms and technologies, the ATMS adapts to dynamic traffic and weather conditions, allowing for proactive and efficient management in real-time.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of An Advanced Traffic Management System (ATMS) in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a flow chart of a method for managing traffic in a road network using an Advanced Traffic Management System (ATMS) in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a diagram showing working communication framework of the proposed advanced traffic management system in accordance with an embodiment of the present disclosure;
Figure 4 illustrates a design interface of said central dashboard system (120) in accordance with an embodiment of the present disclosure;
Figure 5 illustrates a design interface of said advanced driver advisory system (ADAS) in accordance with an embodiment of the present disclosure;and
Figure 6 illustrates a design interface of said automatic traffic classification and counter (ATCC) in accordance with an embodiment of the present disclosure.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

Detailed Description
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those 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 restrictive thereof.
Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, 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 illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
The functional units described in this specification have been labeled as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may include executable code and may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executable of an identified device need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the device and achieve the stated purpose of the device.
Indeed, an executable code of a device or module could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices. Similarly, operational data may be identified and illustrated herein within the device, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, as electronic signals on a system or network.
Reference throughout this specification to “a select embodiment,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Thus, appearances of the phrases “a select embodiment,” “in one embodiment,” or “in an embodiment” in various places throughout this specification are not necessarily referring to the same embodiment.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided, to provide a thorough understanding of embodiments of the disclosed subject matter. One skilled in the relevant art will recognize, however, that the disclosed subject matter can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.
In accordance with the exemplary embodiments, the disclosed computer programs or modules can be executed in many exemplary ways, such as an application that is resident in the memory of a device or as a hosted application that is being executed on a server and communicating with the device application or browser via a number of standard protocols, such as TCP/IP, HTTP, XML, SOAP, REST, JSON and other sufficient protocols. The disclosed computer programs can be written in exemplary programming languages that execute from memory on the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages such as JavaScript, Python, Ruby, PHP, Perl or other sufficient programming languages.
Some of the disclosed embodiments include or otherwise involve data transfer over a network, such as communicating various inputs or files over the network. The network may include, for example, one or more of the Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and/or any other delivery or tunneling mechanism for carrying data. The network may include multiple networks or sub networks, each of which may include, for example, a wired or wireless data pathway. The network may include a circuit-switched voice network, a packet-switched data network, or any other network able to carry electronic communications. For example, the network may include networks based on the Internet protocol (IP) or asynchronous transfer mode (ATM), and may support voice using, for example, VoIP, Voice-over-ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular telephone network configured to enable exchange of text or SMS messages.
Examples of the network include, but are not limited to, a personal area network (PAN), a storage area network (SAN), a home area network (HAN), a campus area network (CAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a virtual private network (VPN), an enterprise private network (EPN), Internet, a global area network (GAN), and so forth.
Figure 1 illustrates a block diagram of an Advanced Traffic Management System (100) (ATMS) in accordance with an embodiment of the present disclosure. The system comprises:a Traffic Monitor Camera System (TMCS) (102) comprising a network of high-resolution cameras (104) strategically positioned along roadways, each equipped with image processing capabilities for real-time traffic monitoring.
In an embodiment, a Video Incident Detection System (VIDS) (106) employs computer vision algorithms for the detection and analysis of incidents, wherein automatic alerts are generated and transmitted to a Central Dashboard System (120).
In an embodiment, a Variable Message Sign (VMS) boards (108) is usedfor displaying dynamic information to drivers, said VMS boards (108) being capable of real-time updates based on information received from the Central Dashboard System (120).
In an embodiment, a Vehicle Actuated Speed Detection (VASD) (110) employs sensors (112) embedded in roadways to detect vehicle speed, with dynamic adjustment of speed limits displayed on VMS boards (108) based on traffic density and road conditions.
In an embodiment, an Advanced Driver Advisory System (ADAS) (114) provides personalized real-time driving advice to users based on location, traffic conditions, and historical data, with integration into an Emergency Calling System (ECS) (126) for enhanced safety during emergencies.
In an embodiment, a Motion Detection System (MDS) (116) utilizes advanced sensors (118) to detect pedestrian and cyclist motion near roadways, with generated alerts transmitted to the Central Dashboard System (120) for proactive safety measures.
In an embodiment, a Central Dashboard System (120) serving as a control center for aggregating data from all subsystems, enabling real-time analytics, incident response coordination, and dynamic traffic management through a user-friendly interface (122).
In an embodiment, a Geographical Information System (GIS) (124) provides spatial data analysis to enhance the accuracy of traffic predictions and optimize routing, with integration into the Central Dashboard System (120) for traffic management.
In an embodiment, an Emergency Calling System (ECS) (126) enables users to initiate emergency calls directly from their vehicles, with location-based data transmitted to emergency services for rapid response.
In an embodiment, anautomatic Traffic Classification and Counter (ATCC)(128) employs advanced sensors to classify vehicles and accurately count traffic flow, with integrated data transmitted to the Central Dashboard System (120) for real-time traffic monitoring and analysis.
In an embodiment, aWeather Monitoringsubsystem (130) integrates data from meteorological sourcesto anticipate and mitigate the impact of adverse weather conditions on traffic, with dynamic updates provided to users through VMS boards (108) and ADAS (114).
In an embodiment, the Traffic Monitor Camera System (TMCS) (102) comprises infrared sensors (102a) for enhanced visibility during low-light conditions, and said sensors are configured to capture and process images in both visible and infrared spectra, wherein the Video Incident Detection System (VIDS) (106) employs machine learning algorithms to continuously adapt to changing traffic scenarios, thereby improving the accuracy of incident detection and reducing false positives, wherein the Variable Message Sign (VMS) boards (108) utilize communication protocols that allow for remote updates and synchronization with the Central Dashboard System (120), ensuring real-time dissemination of critical information to drivers.
In an embodiment, the Vehicle Actuated Speed Detection (VASD) (110) employs machine learning models to predict and adapt to traffic patterns, dynamically adjusting speed limits based on historical and real-time traffic data, wherein the Advanced Driver Advisory System (ADAS) (114) utilizes data from the Motion Detection System (MDS) (116) to provide enhanced warnings and suggestions to drivers regarding the presence of pedestrians and cyclists in proximity to the roadway, and wherein the Central Dashboard System (120) employs advanced analytics, including predictive modeling, to anticipate traffic congestion and dynamically reroute traffic to optimize overall traffic flow.
In an embodiment, the Emergency Calling System (ECS) (126) integrates with vehicle telematics to transmit additional relevant information, such as vehicle type, occupant details, and collision severity, to emergency services during an emergency call, wherein the Automatic Traffic Classification and Counter (ATCC) (128) employs machine learning algorithms to adaptively classify new vehicle types and improve accuracy in traffic flow monitoring, and wherein the Weather Monitoring subsystem (130) integrates data from multiple meteorological sources, including satellite imagery and ground-based sensors, to provide a complete understanding of weather conditions and their impact on traffic.
In an embodiment, the Traffic Monitor Camera System (TMCS) (102), Video Incident Detection System (VIDS) (106), Variable Message Sign (VMS) boards (108), Vehicle Actuated Speed Detection (VASD) (110), Advanced Driver Advisory System (ADAS) (114), Motion Detection System (MDS) (116), Central Dashboard System (120), Geographical Information System (GIS) (124), Emergency Calling System (ECS) (126), automatic Traffic Classification and Counter (ATCC) (128), and Weather Monitoring subsystem (130)may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like.
Figure 2 illustrates a flow chart of a method (200) for managing traffic in a road network using an Advanced Traffic Management System (ATMS) (100) in accordance with an embodiment of the present disclosure. At step (202)the method (200) includes capturing real-time images of roadways using the Traffic Monitor Camera System (TMCS).
At step (204) the method (200) includes processing the captured images for traffic monitoring purposes.
At step (206) the method (200) includes employing the Video Incident Detection System (VIDS) with computer vision algorithms to detect and analyze traffic incidents.
At step (208) the method (200) includes generating automatic alerts for incident response coordination based on the detected traffic incidents.
At step (210) the method (200) includes displaying dynamic information to drivers through Variable Message Sign (VMS) boards.
At step (212) the method (200) includes updating the displayed information in real-time based on data received from a Central Dashboard System.
At step (214) the method (200) includes utilizing the Vehicle Actuated Speed Detection (VASD) system with sensors embedded in roadways to detect vehicle speed.
At step (216) the method (200) includes dynamically adjusting speed limits displayed on VMS boards based on traffic density and road conditions.
At step (218) the method (200) includes providing personalized real-time driving advice to users through the Advanced Driver Advisory System (ADAS) based on their location, traffic conditions, and historical data.
In an embodiment, the method (200) further comprises employing the Motion Detection System (MDS) with advanced sensors to detect pedestrian and cyclist motion near roadways; and aggregating data from all subsystems into a Central Dashboard System and performing real-time analytics for incident response coordination and dynamic traffic management based on the aggregated data.
In an embodiment, the method (200) further comprises enhancing traffic predictions and optimizing routing through spatial data analysis using a Geographical Information System (GIS) integrated into the Central Dashboard System; and enabling users to initiate emergency calls directly from their vehicles through an Emergency Calling System (ECS) and transmitting location-based data to emergency services for rapid response.
In an embodiment, the method (200) further comprises employing an Automatic Traffic Classification and Counter (ATCC) to classify vehicles and accurately count traffic flow.
In an embodiment, the method (200) further comprises integrating data from meteorological sources into the ATMS to anticipate and mitigate the impact of adverse weather conditions on traffic.
In an embodiment, the method (200) further comprises providing dynamic updates to users through VMS boards and ADAS based on the anticipated and mitigated impact of adverse weather conditions on traffic.
Figure 3 illustrates a diagram showing working communication framework of the proposed advanced traffic management system in accordance with an embodiment of the present disclosure.
Referring to figure 3, each part of communication, is connected to internet, through which they communicate with each other. The vehicular enforcement agencies, and their component is connected to component, wherein said detection system comprising sensors are connected to internet through a network antenna. The workstations and server in control rooms are also connected to internet. The mobile application is also connected to internet. The ATMS system was created using a three-tier architecture. The Central Control Room (CCR) is the databank for all the data collected from the field equipment and its processing and archiving. ATMS is a system built for road safety. Real-time traffic information from several sources, including as cameras, ATCC, VMS, CCTV, VIDS, VADS, weather, ADAS, and ANPR. flows into the Central Control Room, where it is processed and integrated, for example, to detect incidents, and may lead to the implementation of traffic outings and VMS messages, which are intended to enhance traffic flow and road safety.
The Advanced Traffic Management System (ATMS) is an integrated solution, designed for road safety enhancement, traffic flow optimization, and real-time information dissemination, with a modular design that allows for scalability and adaptability, establishing it as a pioneering system in the field of traffic management.
The Traffic Monitor Camera System (TMCS) is a sophisticated infrastructure comprising a network of strategically placed high-resolution cameras along roadways. Each camera is equipped with image processing capabilities for real-time traffic monitoring. The integration with the Central Dashboard System enables instant analysis of traffic conditions. The Video Incident Detection System (VIDS) utilizes computer vision algorithms to detect and analyze incidents, automatically generating alerts for immediate response from the Central Dashboard System. Variable Message Sign (VMS) Boards display dynamic information to drivers, with real-time updates based on the current traffic situation. The Vehicle Actuated Speed Detection (VASD) system uses sensors in roadways to detect vehicle speed, dynamically adjusting speed limits displayed on VMS boards. The Advanced Driver Advisory System (ADAS) provides personalized real-time driving advice based on location and historical data, enhancing safety and integrating with the Emergency Calling System for immediate assistance. The Motion Detection System (MDS) uses advanced sensors to detect pedestrian and cyclist motion, generating alerts for proactive safety measures. The Central Dashboard System serves as the control center, aggregating data from all subsystems for real-time analytics, incident response coordination, and dynamic traffic management. The Geographical Information System (GIS) enhances traffic predictions and routing through spatial data analysis. The Emergency Calling System (ECS) enables users to initiate emergency calls, transmitting location-based data for rapid response. The Automatic Traffic Classification and Counter (ATCC) accurately classifies vehicles and counts traffic flow, integrated into the Central Dashboard System for monitoring and analysis. Weather Monitoring integrates meteorological data to anticipate and mitigate the impact of adverse weather conditions on traffic, providing dynamic updates through VMS boards and ADAS.
The VIDS employs advanced computer vision algorithms to analyze video feeds for precise incident detection. These algorithms often include components like object detection, image segmentation, and pattern recognition. Object detection algorithms, such as YOLO (You Only Look Once) or Faster R-CNN (Region-based Convolutional Neural Network), can identify and locate objects in real-time. Image segmentation techniques, like Mask R-CNN, can help in distinguishing and tracking different elements in the video, such as vehicles and pedestrians. Pattern recognition algorithms analyze the temporal patterns of movement to identify abnormal events, such as sudden stops or irregular traffic flow. These algorithms are often implemented using deep learning frameworks like TensorFlow or PyTorch.
The ATCC utilizes machine learning models for adaptive vehicle classification. This involves data collection from various sensors, including cameras and perhaps lidar or radar, to capture diverse traffic scenarios. Feature engineering is crucial, involving the extraction of relevant information such as vehicle speed, size, and trajectory. The machine learning models, which could be neural networks or decision trees, are trained on this data to classify vehicles dynamically. The ADAS, for personalized real-time driving advice, incorporates machine learning models that consider individual driving behavior, traffic conditions, and possibly even weather data. These models continuously learn and adapt based on new data, ensuring improved accuracy over time.
The GIS component enhances traffic predictions and routing optimization through spatial data analysis. Geographical data, including real-time traffic information and historical data, is integrated into the GIS system. Spatial analysis involves techniques like spatial interpolation to estimate traffic conditions between known data points. Routing optimization algorithms use this spatial analysis to suggest the most efficient routes based on current traffic conditions. GIS tools like ArcGIS or open-source libraries like GeoPandas in Python may be employed for handling and analyzing spatial data. Integration with real-time data streams ensures that the system can dynamically adjust predictions and route recommendations.
The TMCS employs infrared sensors for efficient traffic monitoring. Infrared sensors detect heat signatures, allowing the system to track the movement of vehicles regardless of lighting conditions. Advanced sensors in the MDS contribute to motion detection, which can be crucial for identifying sudden changes in traffic patterns that may indicate incidents. The MDS may use technologies like LiDAR (Light Detection and Ranging) or radar for high-precision motion sensing. Signal processing techniques are often employed to filter and interpret the sensor data, providing accurate and timely information to the overall traffic management system.
Variable Message Sign (VMS) boards are equipped with advanced communication protocols to facilitate real-time updates. These protocols define the rules and conventions for exchanging data between the Central Dashboard System and the VMS boards. Common communication protocols used in such systems include WebSocket, which enables bidirectional communication, and MQTT (Message Queuing Telemetry Transport), a lightweight and efficient protocol for publish-subscribe communication. These protocols ensure seamless and rapid transmission of data, allowing VMS boards to display up-to-date information to drivers and contribute to effective traffic management.
The Emergency Calling System (ECS) integrates with vehicle telematics, creating a synergistic connection between the emergency response system and the data collected from vehicles. Telematics involves the use of telecommunications and informatics in vehicles to transmit, receive, and store information. In the context of ECS, vehicle telematics provide crucial data during emergency calls, such as the vehicle's location, speed, and potentially sensor information. This integration enhances emergency response capabilities by delivering real-time information to emergency services, allowing for quicker and more informed decision-making.
Weather Integration involves the incorporation of meteorological data and advanced algorithms within the traffic management system. Meteorological sources provide real-time information about weather conditions, including factors such as temperature, precipitation, and wind speed. Advanced algorithms analyze this data to anticipate and mitigate the impact of adverse weather conditions on traffic. For example, predictive modeling may be used to forecast how weather conditions could affect road surfaces, leading to adjustments in traffic flow management. This integration ensures that the traffic management system can proactively respond to changing weather conditions, enhancing safety and efficiency on the road.
These technologies collectively create a sophisticated traffic management system that ensures efficient incident detection, adaptive vehicle classification, personalized driving advice, and optimized routing. The integration of these technologies and algorithms within the ATMS distinguishes it as an improved solution in the field of traffic management, addressing the shortcomings of existing systems.
The proposed system employs plurality of dashboards for the working of the proposed Advanced Traffic Management System (ATMS).
Figure 4 illustrates a design interface of said central dashboard system (120) in accordance with an embodiment of the present disclosure.
Referring to figure 4, the design interface of said central dashboard system includes a GIS MAP interface, an interface through which interface of other systems can be selected, an interface showing the violations done by vehicle under subject and a notification interface.
Figure 5 illustrates a design interface of said advanced driver advisory system (ADAS) in accordance with an embodiment of the present disclosure.
Referring to figure 5, said design interface of advanced driver advisory system includes of a menu interface, and an interface showing maps.
Figure 6 illustrates a design interface of said automatic traffic classification and counter (ATCC) in accordance with an embodiment of the present disclosure.
Referring to figure 6, said design interface of automatic traffic classification and counter (ATCC) includes a menu interface, and a dashboard showing different types of vehicle, and sub-interface showing details about said types of vehicles.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
, Claims:1. An Advanced Traffic Management System (ATMS) comprising:
a. A Traffic Monitor Camera System (TMCS) comprising a network of high-resolution cameras strategically positioned along roadways, each equipped with image processing capabilities for real-time traffic monitoring;
b. A Video Incident Detection System (VIDS) employing computer vision algorithms for the detection and analysis of incidents, wherein automatic alerts are generated and transmitted to a Central Dashboard System;
c. Variable Message Sign (VMS) boards for displaying dynamic information to drivers, said VMS boards being capable of real-time updates based on information received from the Central Dashboard System;
d. Vehicle Actuated Speed Detection (VASD) employing sensors embedded in roadways to detect vehicle speed, with dynamic adjustment of speed limits displayed on VMS boards based on traffic density and road conditions;
e. An Advanced Driver Advisory System (ADAS) providing personalized real-time driving advice to users based on location, traffic conditions, and historical data, with integration into an Emergency Calling System (ECS) for enhanced safety during emergencies;
f. A Motion Detection System (MDS) utilizing advanced sensors to detect pedestrian and cyclist motion near roadways, with generated alerts transmitted to the Central Dashboard System for proactive safety measures;
g. A Central Dashboard System serving as a control center for aggregating data from all subsystems, enabling real-time analytics, incident response coordination, and dynamic traffic management through a user-friendly interface;
h. A Geographical Information System (GIS) providing spatial data analysis to enhance the accuracy of traffic predictions and optimize routing, with integration into the Central Dashboard System for traffic management;
i. An Emergency Calling System (ECS) enabling users to initiate emergency calls directly from their vehicles, with location-based data transmitted to emergency services for rapid response;
j. Automatic Traffic Classification and Counter (ATCC) employing advanced sensors to classify vehicles and accurately count traffic flow, with integrated data transmitted to the Central Dashboard System for real-time traffic monitoring and analysis;
k. Weather Monitoring subsystem integrating data from meteorological sources to anticipate and mitigate the impact of adverse weather conditions on traffic, with dynamic updates provided to users through VMS boards and ADAS.

2. The Advanced Traffic Management System (ATMS) of claim 1, wherein the Traffic Monitor Camera System (TMCS) comprises infrared sensors for enhanced visibility during low-light conditions, and said sensors are configured to capture and process images in both visible and infrared spectra, wherein the Video Incident Detection System (VIDS) employs machine learning algorithms to continuously adapt to changing traffic scenarios, thereby improving the accuracy of incident detection and reducing false positives, wherein the Variable Message Sign (VMS) boards utilize communication protocols that allow for remote updates and synchronization with the Central Dashboard System, ensuring real-time dissemination of critical information to drivers.

3. The Advanced Traffic Management System (ATMS) of claim 1, wherein the Vehicle Actuated Speed Detection (VASD) employs machine learning models to predict and adapt to traffic patterns, dynamically adjusting speed limits based on historical and real-time traffic data, wherein the Advanced Driver Advisory System (ADAS) utilizes data from the Motion Detection System (MDS) to provide enhanced warnings and suggestions to drivers regarding the presence of pedestrians and cyclists in proximity to the roadway, and wherein the Central Dashboard System employs advanced analytics, including predictive modeling, to anticipate traffic congestion and dynamically reroute traffic to optimize overall traffic flow.

4. The Advanced Traffic Management System (ATMS) of claim 1, wherein the Emergency Calling System (ECS) integrates with vehicle telematics to transmit additional relevant information, such as vehicle type, occupant details, and collision severity, to emergency services during an emergency call, wherein the Automatic Traffic Classification and Counter (ATCC) employs machine learning algorithms to adaptively classify new vehicle types and improve accuracy in traffic flow monitoring, and wherein the Weather Monitoring subsystem integrates data from multiple meteorological sources, including satellite imagery and ground-based sensors, to provide a complete understanding of weather conditions and their impact on traffic.

5. A method for managing traffic in a road network using an Advanced Traffic Management System (ATMS), comprising:
a. Capturing real-time images of roadways using the Traffic Monitor Camera System (TMCS);
b. Processing the captured images for traffic monitoring purposes;
c. Employing the Video Incident Detection System (VIDS) with computer vision algorithms to detect and analyze traffic incidents;
d. Generating automatic alerts for incident response coordination based on the detected traffic incidents;
e. Displaying dynamic information to drivers through Variable Message Sign (VMS) boards;
f. Updating the displayed information in real-time based on data received from a Central Dashboard System;
g. Utilizing the Vehicle Actuated Speed Detection (VASD) system with sensors embedded in roadways to detect vehicle speed;
h. Dynamically adjusting speed limits displayed on VMS boards based on traffic density and road conditions;
i. Providing personalized real-time driving advice to users through the Advanced Driver Advisory System (ADAS) based on their location, traffic conditions, and historical data.

6. The method of Claim 5, further comprising employing the Motion Detection System (MDS) with advanced sensors to detect pedestrian and cyclist motion near roadways; and aggregating data from all subsystems into a Central Dashboard System and performing real-time analytics for incident response coordination and dynamic traffic management based on the aggregated data.

7. The method of Claim 5, further comprising enhancing traffic predictions and optimizing routing through spatial data analysis using a Geographical Information System (GIS) integrated into the Central Dashboard System; and enabling users to initiate emergency calls directly from their vehicles through an Emergency Calling System (ECS) and transmitting location-based data to emergency services for rapid response.

8. The method of Claim 5, further comprising employing an Automatic Traffic Classification and Counter (ATCC) to classify vehicles and accurately count traffic flow.

9. The method of Claim 5, further comprising integrating data from meteorological sources into the ATMS to anticipate and mitigate the impact of adverse weather conditions on traffic.

10. The method of Claim5, further comprising providing dynamic updates to users through VMS boards and ADAS based on the anticipated and mitigated impact of adverse weather conditions on traffic.