ECO-FRIENDLY ROUTING SYSTEM FOR URBAN TRANSPORTATION USING DIGITAL TWIN

Abstract

ABSTRACT The present disclosure introduces an eco-friendly routing system for urban transportation using digital twin, which dynamically optimizes routes to minimize environmental impact. The system comprises of GPS sensor 102 for real-time vehicle positioning and environmental sensors 104 to monitor gases and particulate matter levels. Traffic data, including vehicle density and road conditions, is captured by camera sensors 106 and processed by microprocessor 108, which executes deep learning and AI algorithms 112 to analyse environmental and traffic patterns. A digital twin model 110 simulates real-time virtual representation of urban road network, continuously updating route recommendations. The IoT network and communication system 114 enables seamless data exchange among components, while the energy monitoring system for electric vehicles 116 tracks EV energy consumption to suggest routes. The user interface 118 provides real-time eco-friendly route options, and community feedback integration system 120 refines recommendations based on user input. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00023] 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.

[00024] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of eco-friendly routing system for urban transportation using digital twin 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.

[00025] 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.

[00026] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or 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 or apparatus.

[00027] 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.

[00028] 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.

[00029] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of GPS sensor 102, environmental sensors 104, camera sensors 106, microprocessor 108, digital twin model 110, deep learning and AI algorithms 112, IoT network and communication system 114, energy monitoring system for electric vehicles 116, user interface 118 and community feedback integration system 120.

[00030] Referring to Fig. 1, the present disclosure provides details of eco-friendly routing system for urban transportation using digital twin 100. It is a comprehensive framework designed to optimize urban routes based on real-time environmental and traffic data, promoting sustainable transportation. The system integrates GPS sensor 102, environmental sensors 104, camera sensors 106, and microprocessor 108 to gather and process data on emissions, energy usage, and traffic conditions. In one embodiment, the eco-friendly routing system utilizes digital twin model 110 to simulate the urban environment and generate eco-friendly route recommendations. The system includes deep learning and AI algorithms 112 for predictive analytics, IOT network and communication system 114 for data transmission and energy monitoring system for electric vehicles 116 to optimize fuel efficiency. The user interface 118 provides real-time navigation feedback, while community feedback integration system 120 enhances adaptability based on collective user insights.

[00031] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with GPS sensor 102, which enables precise location tracking of vehicles in real time. The GPS sensor 102 collects positional data necessary for route mapping, allowing other components to analyze vehicle location and suggest optimized routes. This sensor interacts with camera sensors 106 to provide accurate traffic and road condition information based on vehicle positioning. It also supplies data to the digital twin model 110 for creating a virtual representation of the city's road network.

[00032] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with environmental sensors 104, which monitor key environmental parameters like CO2, NOx, particulate matter, and other pollutants. These sensors 104 gather emission and air quality data across different parts of the city, enabling real-time monitoring of environmental impact. Data from environmental sensors 104 is processed by the microprocessor 108 and fed into the digital twin model 110 to generate eco-friendly routes. Additionally, these sensors work in conjunction with energy monitoring system for electric vehicles 116 to assess the environmental footprint of specific routes.

[00033] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with camera sensors 106, which capture traffic density, vehicle types, and road conditions at strategic points in the city. These AI-enabled cameras 106 utilize image recognition to detect traffic congestion and driving patterns that may affect emissions. Camera sensors 106 send traffic data to the microprocessor 108, where deep learning and AI algorithms 112 analyze it for route optimization. They also work closely with GPS sensor 102 to provide location-based traffic insights to the digital twin model 110.

[00034] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with microprocessor 108, which is responsible for processing sensor data and executing AI models. The microprocessor 108 analyzes inputs from environmental sensors 104, camera sensors 106, and GPS sensor 102 to generate actionable insights on optimal routes. Equipped with deep learning and AI algorithms 112, the microprocessor 108 continuously refines route suggestions based on environmental and traffic conditions. It feeds this processed data into the digital twin model 110 to dynamically update route recommendations.

[00035] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with digital twin model 110, which creates a real-time virtual representation of the city's road network and environmental conditions. This digital model 110 incorporates data from GPS sensor 102, environmental sensors 104, and camera sensors 106 to simulate current traffic and environmental conditions. The digital twin model 110 dynamically updates based on data processed by the microprocessor 108, enabling the system to suggest eco-friendly routes. It serves as the core of the routing system, connecting all components to create an integrated, responsive navigation solution.

[00036] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with deep learning and AI algorithms 112, which are used to analyze data from various sensors and predict traffic and emission patterns. These algorithms optimize routes by assessing factors like congestion and environmental impact, making the route recommendations adaptive to changing conditions. Deep learning and AI algorithms 112 work within the microprocessor 108 and communicate with the digital twin model 110 to ensure route recommendations are both efficient and environmentally conscious.

[00037] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin 100 is provided with IOT network and communication system 114, which facilitates seamless data exchange between all sensors, microprocessor 108, and digital twin model 110. The IOT network and communication system 114 enables real-time updates by connecting GPS sensor 102, environmental sensors 104, and camera sensors 106 to the microprocessor 108. This ensures that data is consistently synchronized, allowing the digital twin model 110 to reflect accurate conditions across the urban network.

[00038] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin is provided with energy monitoring system for electric vehicles 116, which tracks the energy consumption and battery levels of EVs. This component 116 helps identify the most energy-efficient routes, especially for electric vehicles. Energy monitoring system for electric vehicles 116 collaborates with environmental sensors 104 to assess routes that minimize energy use and environmental impact. Data from this system is also processed by the microprocessor 108 for integration into the digital twin model 110.

[00039] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin is provided with user interface 118, which displays navigation and route recommendations to drivers in real time. The user interface 118 provides feedback based on data from the digital twin model 110 and processed insights from microprocessor 108. It allows drivers to select eco-friendly routes and adapts to changes in traffic and environmental conditions. The user interface 118 serves as the primary point of interaction for users, presenting optimized, sustainable route options.

[00040] Referring to Fig. 1, eco-friendly routing system for urban transportation using digital twin is provided with community feedback integration system 120, which collects user feedback to improve route recommendations and system adaptability. By gathering insights from drivers, the community feedback integration system 120 refines the digital twin model 110 over time, making it more responsive to user needs. It allows the system to adapt based on collective experiences, enhancing the accuracy and relevance of route suggestions.

[00041] Referring to Fig 2, there is illustrated method 200 for eco-friendly routing system for urban transportation using digital twin 100. The method comprises:

At step 202, method 200 includes GPS sensor 102 determining the real-time location of the vehicle and providing positional data to the system;
At step 204, method 200 includes environmental sensors 104 collecting data on air quality parameters such as CO2, NOx, and particulate matter levels around the vehicle's location;
At step 206, method 200 includes camera sensors 106 capturing traffic density, vehicle types, and road conditions at key points in the city, transmitting this data to the microprocessor 108;
At step 208, method 200 includes microprocessor 108 processing data from GPS sensor 102, environmental sensors 104, and camera sensors 106 to analyze current traffic and environmental conditions;
At step 210, method 200 includes digital twin model 110 integrating processed data to create a real-time virtual representation of the city's road network and simulating the most eco-friendly route options;
At step 212, method 200 includes deep learning and AI algorithms 112 within the microprocessor 108 predicting traffic patterns and emissions based on current data to optimize route recommendations;
At step 214, method 200 includes IOT network and communication system 114 enabling real-time data exchange between GPS sensor 102, environmental sensors 104, camera sensors 106, and microprocessor 108 for consistent updates;
At step 216, method 200 includes energy monitoring system for electric vehicles 116 tracking the energy consumption of EVs and sending data to microprocessor 108 to assist in selecting energy-efficient routes;
At step 218, method 200 includes user interface 118 displaying eco-friendly route recommendations to the driver, updating suggestions based on real-time data from the digital twin model 110;
At step 220, method 200 includes community feedback integration system 120 gathering user feedback on route effectiveness, further refining route recommendations within digital twin model 110 for future use.

[00042] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00043] 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.

[00044] 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. An eco- friendly routing system for urban transportation using digital twin 100 comprising of
GPS sensor 102 to provide real-time vehicle location for accurate route mapping;
environmental sensors 104 to monitor air quality parameters like CO2 and NOx levels;
camera sensors 106 to capture traffic density and road conditions;
microprocessor 108 to process sensor data and execute AI models;
digital twin model 110 to create a real-time virtual representation of the urban road network;
deep learning and AI algorithms 112 to predict traffic patterns and optimize routes;
IoT network and communication system 114 to enable real-time data exchange among components;
energy monitoring system for electric vehicles 116 to track energy consumption and assist in selecting efficient routes;
user interface 118 to display eco-friendly route recommendations to the driver; and
community feedback integration system 120 to gather user insights for refining route suggestions.

2. The eco-friendly routing system for urban transportation using digital twin 100 as claimed, wherein GPS sensor 102 is configured to provide real-time, high-precision vehicle location data that supports dynamic route adjustments, allowing seamless integration with environmental and traffic data for eco-friendly navigation.

3. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein environmental sensors 104 are configured to measure CO2, NOx, and particulate matter levels continuously and communicate with the digital twin model 110 to create route recommendations that minimize exposure to high pollution zones, reducing the vehicle's environmental footprint.

4. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein camera sensors 106 are configured to capture data on traffic density, vehicle classification, and road conditions in real time, transmitting data to the microprocessor 108 for continuous traffic pattern analysis, enabling the system to avoid high-traffic zones dynamically.

5. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein microprocessor 108 is configured to execute deep learning and AI algorithms 112 to analyze incoming data from environmental and traffic sensors, continuously predicting optimal, eco-friendly routes based on both vehicle type and current environmental impact.

6. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein digital twin model 110 is configured as a real-time, virtual urban model that integrates traffic, environmental, and vehicle telemetry data to simulate and recommend routes with minimal emissions, continuously updating routes based on real-time urban dynamics.

7. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein deep learning and AI algorithms 112 within microprocessor 108 are configured to predict traffic congestion and emission levels, optimizing route recommendations that reduce idle time, fuel consumption, and exposure to high-pollution areas.

8. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein IOT network and communication system 114 is configured to establish continuous, low-latency data transmission among GPS sensor 102, environmental sensors 104, camera sensors 106, and microprocessor 108, enabling real-time, synchronized updates across all system components for adaptive route optimization.

9. The eco-friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein user interface 118 is configured to provide drivers with updated, eco-friendly route recommendations in real time, incorporating data from community feedback integration system 120 to improve route accuracy and environmental impact through continuous user-driven refinements.

10. The eco- friendly routing system for urban transportation using digital twin 100 as claimed in claim 1, wherein method comprises of
GPS sensor 102 determining the real-time location of the vehicle and providing positional data to the system;
environmental sensors 104 collecting data on air quality parameters such as CO2, NOx, and particulate matter levels around the vehicle's location;
camera sensors 106 capturing traffic density, vehicle types, and road conditions at key points in the city, transmitting this data to the microprocessor 108;
microprocessor 108 processing data from gps sensor 102, environmental sensors 104, and camera sensors 106 to analyze current traffic and environmental conditions;
digital twin model 110 integrating processed data to create a real-time virtual representation of the city's road network and simulating the most eco-friendly route options;
deep learning and AI algorithms 112 within the microprocessor 108 predicting traffic patterns and emissions based on current data to optimize route recommendations;
IOT network and communication system 114 enabling real-time data exchange between GPS sensor 102, environmental sensors 104, camera sensors 106, and microprocessor 108 for consistent updates;
energy monitoring system for electric vehicles 116 tracking the energy consumption of EVs and sending data to microprocessor 108 to assist in selecting energy-efficient routes;
user interface 118 displaying eco-friendly route recommendations to the driver, updating suggestions based on real-time data from the digital twin model 110; and
community feedback integration system 120 gathering user feedback on route effectiveness, further refining route recommendations within digital twin model 110 for future use

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