METHOD AND SYSTEM FOR LOCAL TRAFFIC REGULATION

Abstract

ABSTRACT The present disclosure discloses a method (100) for local traffic regulation comprising: collecting real-time data from vehicle ECUs and sensors (102) via OBD II interfaces, processing the collected data at an edge computing layer within vehicles (104), transmitting processed data from the edge computing layer to a fog computing layer (106), aggregating and analyzing data from multiple vehicles at the fog computing layer (108), generating traffic optimization strategies based on the analyzed data (110), transmitting optimization instructions back to vehicles and traffic management systems (112), and implementing the optimization instructions to regulate local traffic and improve vehicle performance (114).

Specification

Description:METHOD AND SYSTEM FOR LOCAL TRAFFIC REGULATION
TECHNICAL FIELD
The present invention relates to traffic management systems, and more particularly to a method and system for local traffic regulation using edge and fog computing.
BACKGROUND
Traffic congestion in urban areas is a growing problem that impacts mobility, fuel consumption, emissions, and quality of life. Traditional centralized traffic management systems often struggle to respond quickly to changing traffic conditions. There is a need for more distributed and responsive approaches to traffic regulation that can leverage real-time data from vehicles.
Recent advances in edge and fog computing architectures present an opportunity to process vehicle data closer to the source and enable more localized traffic optimization. However, existing solutions have not fully integrated these computing paradigms with vehicle On-Board Diagnostic (OBD-II) systems and city-wide traffic management.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks. Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.
SUMMARY
The present invention provides a method and system for local traffic regulation using a hybrid edge-fog-cloud computing architecture. Real-time data from vehicle ECUs and sensors is collected via OBD-II interfaces and processed at an edge computing layer within vehicles. The processed data is then transmitted to a fog computing layer comprising multiple nodes throughout a city. The fog layer aggregates and analyzes data from multiple vehicles to generate traffic optimization strategies. These strategies are then transmitted back to vehicles and traffic management systems for implementation.
In accordance with the first aspect of the present disclosure, there is provided a method for local traffic regulation, the method comprising: collecting real-time data from vehicle ECUs and sensors (102) via OBD II interfaces; processing the collected data at an edge computing layer within vehicles; transmitting processed data from the edge computing layer to a fog computing layer; aggregating and analyzing data from multiple vehicles at the fog computing layer; generating traffic optimization strategies based on the analyzed data; transmitting optimization instructions back to vehicles and traffic management systems; and implementing the optimization instructions to regulate local traffic and improve vehicle performance.
Advantageously, the method is used for local traffic regulation lies in its innovative use of a distributed computing architecture that combines edge, fog, and cloud computing to optimize traffic flow and vehicle performance in real-time. By processing data at the edge (within vehicles) and aggregating it at the fog layer (local hubs), the system reduces latency and bandwidth usage compared to purely cloud-based solutions. This approach enables rapid response to changing traffic conditions, allowing for immediate adjustments to individual vehicle performance and coordinated traffic management across multiple vehicles and city infrastructure. The integration of OBD-II data provides detailed insights into vehicle behavior and performance, enabling more precise and effective optimization strategies. Furthermore, the hierarchical structure of edge-fog-cloud computing allows for scalable and flexible deployment, making it adaptable to various urban environments and traffic scenarios. Moreover, such comprehensive approach results in improved traffic flow, reduced emissions, enhanced fuel efficiency, and a more responsive and intelligent urban transportation system.
Optionally, the edge computing layer performs real-time optimization of vehicle performance based on current traffic conditions and driver behavior.

Optionally, the fog computing layer is implemented at traffic management centers, smart city infrastructure, or mobile cell towers.
Optionally, the method further comprising transmitting critical insights and important data from the fog computing layer to a cloud computing layer for long-term trend analysis and model updates.
Optionally, the optimization instructions include suggestions for fuel-efficient driving patterns and alternative routes to reduce emissions and congestion.
Optionally, the method further comprising integrating the fog computing layer with smart traffic regulation and city management systems to optimize overall traffic flow.
Optionally, the method further comprising monitoring real-time emissions data to identify pollution sources and initiate corrective measures.
Optionally, the method further comprising generating proactive maintenance suggestions for vehicles based on local driving conditions, climate, and road quality.
Optionally, the method further comprising automatically scheduling maintenance appointments at nearby service centers based on pre-diagnosed issues.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates a representation of a schematical illustration of a flowchart of a method for local traffic regulation.
FIG.2 illustrates system for local traffic regulation.
FIG. 3 illustrates a diagram of the edge computing layer architecture for the local traffic regulation system, in accordance with an embodiment of the present disclosure.
FIG. 4 illustrates the diagram of the fog computing layer architecture for the local traffic regulation system.
FIG. 5, illustrates a hierarchical data flow system for vehicle monitoring across multiple cities.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system for soilless agriculture and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
The terms "comprise", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or product recommendation system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system for soilless agriculture.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings, and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
Referring to Figure 1, there is illustrated a representation of a schematical illustration of a flowchart of a method for local traffic regulation, in accordance with an embodiment of the present disclosure.
There is provided a method 100 for local traffic regulation. At step 102, the method 100 includes collecting real-time data from vehicle ECUs and sensors via OBD II interfaces. At step 104, the method 100 includes processing the collected data at an edge computing layer within vehicles and at step 106, the method 100 includes transmitting processed data from the edge computing layer to a fog computing layer and aggregating and analyzing data from multiple vehicles at the fog computing layer, such as at step 108. Furthermore, at step 110, the method 100 includes generating traffic optimization strategies based on the analyzed data, transmitting optimization instructions back to vehicles and traffic management systems (at step 112) and implementing the optimization instructions to regulate local traffic and improve vehicle performance (at step 114).
Optionally, the edge computing layer performs real-time optimization of vehicle performance based on current traffic conditions and driver behavior.
Optionally, the fog computing layer is implemented at traffic management centers, smart city infrastructure, or mobile cell towers.
Optionally, the method further comprising transmitting critical insights and important data from the fog computing layer to a cloud computing layer for long-term trend analysis and model updates.
Optionally, the optimization instructions include suggestions for fuel-efficient driving patterns and alternative routes to reduce emissions and congestion.
Optionally, the method further comprising integrating the fog computing layer with smart traffic regulation and city management systems to optimize overall traffic flow.
Optionally, the method further comprising monitoring real-time emissions data to identify pollution sources and initiate corrective measures.
Optionally, the method further comprising generating proactive maintenance suggestions for vehicles based on local driving conditions, climate, and road quality.
Optionally, the method further comprising automatically scheduling maintenance appointments at nearby service centers based on pre-diagnosed issues.
Advantageously, the method 100 is used for local traffic regulation lies in its innovative use of a distributed computing architecture that combines edge, fog, and cloud computing to optimize traffic flow and vehicle performance in real-time. By processing data at the edge (within vehicles) and aggregating it at the fog layer (local hubs), the system reduces latency and bandwidth usage compared to purely cloud-based solutions. This approach enables rapid response to changing traffic conditions, allowing for immediate adjustments to individual vehicle performance and coordinated traffic management across multiple vehicles and city infrastructure. The integration of OBD-II data provides detailed insights into vehicle behavior and performance, enabling more precise and effective optimization strategies. Furthermore, the hierarchical structure of edge-fog-cloud computing allows for scalable and flexible deployment, making it adaptable to various urban environments and traffic scenarios. Moreover, such comprehensive approach results in improved traffic flow, reduced emissions, enhanced fuel efficiency, and a more responsive and intelligent urban transportation system.
Referring to FIG. 2, there is shown a system for local traffic regulation, in accordance with an embodiment of the present disclosure. With reference to FIG. 2, there is shown a system 202 for local traffic regulation.
The system 202 includes a plurality of edge computing devices 20 installed in vehicles, each connected to the vehicle's OBD-II port and configured to collect and process real-time data from the vehicle's ECU and sensors, a fog computing layer 206 comprising multiple fog nodes located within the city, configured to receive processed data from the edge computing devices and perform localized analysis, a cloud computing layer 208 configured to receive critical insights from the fog computing layer and perform long-term trend analysis, a traffic management module 210 integrated with the fog computing layer, configured to generate and implement traffic optimization strategies and a user interface module 212 configured to provide access to the system for local authorities and other stakeholders through mobile applications and web browsers.
Advantageously, the system 202 is used for local traffic regulation lies in its innovative use of a distributed computing architecture that combines edge, fog, and cloud computing to optimize traffic flow and vehicle performance in real-time. By processing data at the edge (within vehicles) and aggregating it at the fog layer (local hubs), the system reduces latency and bandwidth usage compared to purely cloud-based solutions. This approach enables rapid response to changing traffic conditions, allowing for immediate adjustments to individual vehicle performance and coordinated traffic management across multiple vehicles and city infrastructure. The integration of OBD-II data provides detailed insights into vehicle behavior and performance, enabling more precise and effective optimization strategies. Furthermore, the hierarchical structure of edge-fog-cloud computing allows for scalable and flexible deployment, making it adaptable to various urban environments and traffic scenarios. Moreover, such comprehensive approach results in improved traffic flow, reduced emissions, enhanced fuel efficiency, and a more responsive and intelligent urban transportation system.
Referring to FIG. 3, there is shown a diagram of the edge computing layer architecture for the local traffic regulation system, in accordance with an embodiment of the present disclosure. The diagram illustrates the data flow and processing at the vehicle level. It begins with various vehicle data inputs such as GPS data, acceleration, speed, idle time, MAF (Mass Air Flow), throttle position, fuel consumption, and emissions. This data is collected and processed by the Torque Pro / OBD II Scanner application. The application interfaces with the vehicle's ECU (Engine Control Unit) to retrieve and process real-time data. After processing, the edge computing layer generates output or receives input from the Fog computing layer, enabling two-way communication for optimized traffic regulation.
Referring to FIG. 4, there is shown a diagram of the fog computing layer architecture for the local traffic regulation system, in accordance with an embodiment of the present disclosure. The diagram depicts the central role of the fog computing layer in data aggregation and processing. The fog layer contains a real-time data logger that stores and manages the incoming data from multiple edge devices (vehicles). The OBD II result optimizer application processes this aggregated data to generate traffic optimization strategies. The fog layer interfaces with both the edge layer (individual vehicles) and the cloud server, acting as an intermediary for data flow and processing. This architecture allows for localized, low-latency data processing while still maintaining connectivity with broader cloud-based systems for more extensive analysis and long-term data storage.
Referring to FIG. 5, illustrates a hierarchical data flow system for vehicle monitoring across multiple cities. The sensors are used to collect data which is then processed by an OBD II scanner and sent to an edge device (smartphone). This edge device communicates with a fog server, which acts as an intermediary between the local network and the cloud server. The fog server can also communicate with another edge device-smartphone setup within the same city. The cloud server at the top level connects to the fog server, for example in City 1 and potentially to another fog server, for example, in City 2, allowing for data aggregation and analysis across different urban areas. This architecture enables efficient local processing via edge and fog computing while also facilitating broader data integration and analysis in the cloud.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including," "comprising," "incorporating," "have," "is" used to describe, and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate. Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A method (100) for local traffic regulation, the method (100) comprising:
collecting real-time data from vehicle ECUs and sensors (102) via OBD II interfaces;
processing the collected data at an edge computing layer within vehicles (104);
transmitting processed data from the edge computing layer to a fog computing layer (106);
aggregating and analyzing data from multiple vehicles at the fog computing layer (108);
generating traffic optimization strategies based on the analyzed data (110);
transmitting optimization instructions back to vehicles and traffic management systems (112); and
implementing the optimization instructions to regulate local traffic and improve vehicle performance (114).

2. The method (100) as claimed in claim 1, wherein the edge computing layer performs real-time optimization of vehicle performance based on current traffic conditions and driver behavior.

3. The method (100) as claimed in claim 1, wherein the fog computing layer is implemented at traffic management centers, smart city infrastructure, or mobile cell towers.

4. The method (100) as claimed in claim 1, further comprising transmitting critical insights and important data from the fog computing layer to a cloud computing layer for long-term trend analysis and model updates.

5. The method (100) as claimed in claim 1, wherein the optimization instructions include suggestions for fuel-efficient driving patterns and alternative routes to reduce emissions and congestion.

6. The method (100) as claimed in claim 1, further comprising integrating the fog computing layer with smart traffic regulation and city management systems to optimize overall traffic flow.

7. The method (100) as claimed in claim 1, further comprising monitoring real-time emissions data to identify pollution sources and initiate corrective measures.

8. The method (100) as claimed in claim 1, further comprising generating proactive maintenance suggestions for vehicles based on local driving conditions, climate, and road quality.

9. The method (100) as claimed in claim 1, further comprising automatically scheduling maintenance appointments at nearby service centers based on pre-diagnosed issues.

10. A system (202) for local traffic regulation, comprising:
a plurality of edge computing devices (204) installed in vehicles, each connected to the vehicle's OBD-II port and configured to collect and process real-time data from the vehicle's ECU and sensors;
a fog computing layer (206) comprising multiple fog nodes located within the city, configured to receive processed data from the edge computing devices and perform localized analysis;
a cloud computing layer (208) configured to receive critical insights from the fog computing layer and perform long-term trend analysis;
a traffic management module (210) integrated with the fog computing layer, configured to generate and implement traffic optimization strategies; and
a user interface module (212) configured to provide access to the system for local authorities and other stakeholders through mobile applications and web browsers.

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