1. A system for intelligent transportation, comprising: a data collection module for collecting traffic information from a plurality of sources; a data processing module for analyzing said traffic information and generating traffic predictions; and a traffic management module for adjusting traffic signals based on said traffic predictions, thereby enhancing traffic flow and reducing congestion.

2. The system of claim 1, further comprising a user interface module for displaying real-time traffic information and recommended routes to drivers.

3. The system of claim 1, wherein said data collection module includes sensors for detecting the presence and movement of vehicles and pedestrians.

4. The system of claim 1, wherein said data processing module uses machine learning algorithms to analyze traffic data and make predictions about traffic patterns and congestion.

5. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals in real-time based on changing traffic conditions and predicted traffic patterns.

6. The system of claim 1, wherein said traffic management module communicates with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level.

7. The system of claim 1, further comprising a feedback module for collecting data on the effectiveness of the traffic management strategies employed by said system, and using said data to refine and improve said strategies over time.

8. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals to prioritize emergency vehicles and public transportation, thereby improving response times and reducing travel times for commuters.

9. A method for enhancing mobility and safety in an intelligent transportation system, comprising: receiving real-time traffic data from a plurality of sources; analyzing said data to identify traffic patterns and potential congestion points; generating route recommendations based on said analysis; and communicating said route recommendations to drivers, thereby optimizing travel time and reducing the risk of accidents. Intelligent Transportation System to Enhance Mobility and Safety Abstract The present invention relates to intelligent transportation systems, and more specifically to systems and methods for enhancing traffic flow, reducing congestion, and improving safety. The system includes a data collection module for gathering traffic information, a data processing module for analyzing traffic data and generating traffic predictions, and a traffic management module for adjusting traffic signals based on the predictions. Additionally, the system includes a user interface module for displaying real-time traffic information and recommended routes to drivers. The system can communicate with other transportation systems to coordinate traffic flow on a regional or national level. The method involves receiving real-time traffic data, analyzing it to identify congestion points and traffic patterns, generating route recommendations, and communicating these recommendations to drivers to optimize travel time and reduce the risk of accidents. The system and method can prioritize emergency vehicles and public transportation, and also includes a feedback module for refining and improving traffic management strategies over time. , Claims:Claims :

1. A system for intelligent transportation, comprising: a data collection module for collecting traffic information from a plurality of sources; a data processing module for analyzing said traffic information and generating traffic predictions; and a traffic management module for adjusting traffic signals based on said traffic predictions, thereby enhancing traffic flow and reducing congestion.

2. The system of claim 1, further comprising a user interface module for displaying real-time traffic information and recommended routes to drivers.

3. The system of claim 1, wherein said data collection module includes sensors for detecting the presence and movement of vehicles and pedestrians.

4. The system of claim 1, wherein said data processing module uses machine learning algorithms to analyze traffic data and make predictions about traffic patterns and congestion.

5. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals in real-time based on changing traffic conditions and predicted traffic patterns.

6. The system of claim 1, wherein said traffic management module communicates with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level.

7. The system of claim 1, further comprising a feedback module for collecting data on the effectiveness of the traffic management strategies employed by said system, and using said data to refine and improve said strategies over time.

8. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals to prioritize emergency vehicles and public transportation, thereby improving response times and reducing travel times for commuters.

9. A method for enhancing mobility and safety in an intelligent transportation system, comprising: receiving real-time traffic data from a plurality of sources; analyzing said data to identify traffic patterns and potential congestion points; generating route recommendations based on said analysis; and communicating said route recommendations to drivers, thereby optimizing travel time and reducing the risk of accidents.

Description:Intelligent Transportation System to Enhance Mobility and Safety
Field of the Invention
[0001] The present invention relates to an intelligent Transportation Systems (ITS) for enhancing mobility and safety through the collection, processing, and analysis of real-time traffic data from multiple sources, and the use of this data to adjust traffic signals and provide route recommendations to drivers. This system also includes the ability to communicate with other transportation systems and devices, prioritize emergency vehicles and public transportation, and collect feedback to refine and improve traffic management strategies over time.
[0001]
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] Intelligent Transportation Systems (ITS) have emerged as a critical solution to address the growing traffic congestion and safety concerns in cities and urban areas. These systems collect real-time traffic data from multiple sources, such as sensors, cameras, and GPS devices, and use this information to optimize traffic flow and provide drivers with route recommendations.
[0004] However, current ITS systems have several limitations that prevent them from being fully effective. One of the main limitations is their inability to communicate with other transportation systems and devices, which can lead to inefficient traffic flow and delays. For example, emergency vehicles may be delayed due to traffic congestion or lack of coordination between transportation systems.
[0005] Another limitation is the lack of prioritization for emergency vehicles and public transportation. In many cases, emergency vehicles are stuck in traffic like other vehicles, which can lead to delays in responding to emergencies. Similarly, public transportation is often subject to delays and disruptions, which can impact the mobility of commuters.
[0006] Finally, current ITS systems have limited ability to collect feedback on the effectiveness of traffic management strategies. This feedback is critical for refining and improving traffic management strategies over time, which can enhance the overall effectiveness of the system.
[0007] The present invention addresses these limitations by providing a comprehensive ITS system that leverages advanced data processing and machine learning algorithms to analyze traffic data and adjust traffic signals in real-time. This system also includes the ability to communicate with other transportation systems and devices, prioritize emergency vehicles and public transportation, and collect feedback to refine and improve traffic management strategies over time.
[0008] In summary, there is a need for a more comprehensive and effective ITS system that can address the growing traffic congestion and safety concerns in cities and urban areas. The present invention provides a solution to these challenges and enhances mobility and safety for drivers, commuters, and emergency responders.
[0009] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00010] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00011] The present invention relates to an intelligent Transportation Systems (ITS) for enhancing mobility and safety through the collection, processing, and analysis of real-time traffic data from multiple sources, and the use of this data to adjust traffic signals and provide route recommendations to drivers. This system also includes the ability to communicate with other transportation systems and devices, prioritize emergency vehicles and public transportation, and collect feedback to refine and improve traffic management strategies over time.
[00012]
[00013] The present invention is directed towards a system and method for intelligent transportation, which aims to enhance traffic flow and reduce congestion, while also improving mobility and safety for drivers. The system comprises three main components: a data collection module, a data processing module, and a traffic management module. The data collection module collects traffic information from various sources, including sensors that detect the presence and movement of vehicles and pedestrians. The data processing module uses machine learning algorithms to analyze the traffic data and generate traffic predictions, identifying traffic patterns and potential congestion points. The traffic management module adjusts traffic signals in real-time based on changing traffic conditions and predicted traffic patterns, with the capability to communicate with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level. The system also includes a user interface module that displays real-time traffic information and recommended routes to drivers.
[00014] In addition to the system, the invention also includes a method for enhancing mobility and safety in an intelligent transportation system. The method involves receiving real-time traffic data from multiple sources, analyzing the data to identify traffic patterns and potential congestion points, generating route recommendations based on the analysis, and communicating the recommendations to drivers to optimize travel time and reduce the risk of accidents. The system and method provide a comprehensive approach to intelligent transportation, with the ability to refine and improve traffic management strategies over time through a feedback module that collects data on the effectiveness of the strategies employed.
[00015] Overall, the system and method have significant implications for improving the efficiency and safety of transportation networks, leading to reduced travel times, decreased congestion, and fewer accidents. The invention has broad applications in various fields, including urban planning, public transportation, and emergency services.
Brief Description of the Drawings
[00016] 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:
[00017] FIG. 1 represents an overview of system for intelligent transportation is a complex enviroement to help manage traffic flow, according to some embodiments of the present disclosure.
[00018] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for enhancing mobility and safety in an intelligent transportation system, according to some embodiments of the present disclosure.
Detailed Description
[00019] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00020] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00021] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00022] The present invention relates to an intelligent Transportation Systems (ITS) for enhancing mobility and safety through the collection, processing, and analysis of real-time traffic data from multiple sources, and the use of this data to adjust traffic signals and provide route recommendations to drivers. This system also includes the ability to communicate with other transportation systems and devices, prioritize emergency vehicles and public transportation, and collect feedback to refine and improve traffic management strategies over time.
[00023] A system 100 for intelligent transportation is a complex, computer-based system that is designed to help manage traffic flow in real-time by collecting and processing traffic data from multiple sources. The system utilizes advanced algorithms to predict traffic patterns and make adjustments to traffic signals, in order to improve traffic flow and reduce congestion. The following description provides further details about the various components and embodiments of such a system.
[00024] In an embodiment, the data collection module 102 is responsible for gathering traffic information from various sources such as traffic cameras, GPS sensors, and other traffic monitoring systems. The collected data is then transmitted to the data processing module for further analysis. In one embodiment, the data collection module includes a network of traffic sensors that are strategically placed throughout the city to provide real-time traffic data.
[00025] In an embodiment, the data processing module 104 is the core of the intelligent transportation system. It receives traffic data from the data collection module and analyzes it using advanced machine learning algorithms. The algorithms are designed to identify patterns in the traffic data, such as the number of vehicles on the road, their speed, and direction of travel. Based on these patterns, the data processing module generates traffic predictions, which are then used by the traffic management module to adjust traffic signals.
[00026] In one embodiment, the data processing module uses a neural network to analyze traffic data. The neural network is trained using historical traffic data, and it is capable of learning from new data in real-time. The neural network can identify patterns and trends that may not be apparent to human analysts, thereby improving the accuracy of the traffic predictions.
[00027] In an embodiment, the traffic management module is responsible for adjusting traffic signals based on the traffic predictions generated by the data processing module. The module uses a set of rules and algorithms to optimize traffic flow and reduce congestion. In one embodiment, the traffic management module uses a centralized control system that can adjust traffic signals at multiple intersections simultaneously.
[00028] The traffic management module can also adjust traffic signals based on real-time events, such as accidents or road closures. For example, if a major accident occurs on a highway, the traffic management module can automatically reroute traffic to alternative routes to avoid congestion.
[00029] The intelligent transportation system described above can be implemented in various ways, depending on the specific needs and requirements of a given city or region. In one embodiment, the system includes a mobile application that provides real-time traffic updates to drivers. The application can use the data collected by the data collection module to provide users with the most efficient route to their destination.
[00030] In another embodiment, the system includes a web-based portal that provides traffic updates to city officials and emergency responders. The portal can display real-time traffic data, including traffic flow, congestion, and accidents. This information can be used to make informed decisions about traffic management and emergency response.
[00031] In yet another embodiment, the system includes a fleet management module that is designed to optimize the movement of commercial vehicles, such as delivery trucks and buses. The fleet management module can use the traffic predictions generated by the data processing module to optimize delivery routes and reduce fuel consumption.
[00032] In summary, the intelligent transportation system described above is a sophisticated, computer-based system that is designed to help manage traffic flow in real-time. The system collects traffic data from multiple sources, processes the data using advanced algorithms, and adjusts traffic signals to improve traffic flow and reduce congestion. The system can be implemented in various ways, depending on the specific needs and requirements of a given city or region.
[00033] An example use case scenario for the system for intelligent transportation could be a city that experiences heavy traffic congestion during rush hour. The city installs the system at multiple traffic intersections to improve traffic flow and reduce congestion.
[00034] The data collection module collects traffic information from various sources, including cameras, sensors, and GPS data from connected vehicles. The data processing module analyzes this information, identifying traffic patterns, volume, and congestion hotspots. Using machine learning algorithms, the module generates traffic predictions for each intersection based on the collected data and historical trends.
[00035] The traffic management module receives these traffic predictions and adjusts traffic signals in real-time to improve traffic flow and reduce congestion. For example, the module may extend green lights for high volume traffic during rush hour and shorten them for lower volume traffic. By continuously adjusting traffic signals based on traffic predictions, the system helps to reduce stop-and-go traffic and minimize delays.
[00036] The system can also provide real-time traffic information to drivers through a mobile app or website, allowing them to plan their routes and avoid congested areas. This can further improve traffic flow by distributing traffic more evenly across different routes.
[00037] By implementing this system, the city is able to improve traffic flow and reduce congestion, leading to shorter travel times, less air pollution, and increased driver safety. Additionally, the city can use the data collected by the system to plan future infrastructure improvements and optimize transportation resources.
[00038] In an embodiment, the system is an intelligent transportation system that aims to enhance traffic flow and reduce congestion. It comprises several modules working together to achieve this goal. The first module is the data collection module, which collects traffic information from a variety of sources. The system utilizes sensors that detect the presence and movement of vehicles and pedestrians. The collected data is then processed by the data processing module, which uses machine learning algorithms to analyze the traffic data and make predictions about traffic patterns and congestion.
[00039] In an embodiment, the traffic management module is responsible for adjusting traffic signals based on the predictions generated by the data processing module. This module is capable of adjusting traffic signals in real-time based on changing traffic conditions and predicted traffic patterns. The system also includes a user interface module, which displays real-time traffic information and recommended routes to drivers, allowing them to avoid congestion and reach their destination more efficiently.
[00040] Moreover, the traffic management module communicates with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level. Additionally, the system includes a feedback module that collects data on the effectiveness of the traffic management strategies employed by the system. This feedback is used to refine and improve the strategies over time, making the system more effective and efficient.
[00041] Lastly, the traffic management module is capable of adjusting traffic signals to prioritize emergency vehicles and public transportation, thereby improving response times and reducing travel times for commuters. This functionality makes the system more efficient and responsive to the needs of the community it serves.
[00042] The method 200 for enhancing mobility and safety in an intelligent transportation system begins with the step 202 of receiving real-time traffic data from various sources such as cameras, sensors, and GPS devices. The data may include information on vehicle speed, volume, and location, as well as road conditions, weather, and accidents. This data is then analyzed to identify traffic patterns and potential congestion points. At step 204, the method generates route recommendations based on the analysis. The route recommendations may include alternative routes or guidance on how to avoid high congestion areas, accidents, or other hazards on the road. The recommendations may be customized based on the driver's preferences, including fastest route or shortest route. At step 206, the method communicates the route recommendations to drivers, through various means such as mobile apps, in-car systems, or roadside signs, thereby optimizing travel time and reducing the risk of accidents. The communication of the recommendations may also be personalized to the driver, such as using preferred language or providing real-time updates on traffic conditions along the recommended route. At step 208, the method may also incorporate machine learning algorithms to improve the accuracy of the traffic pattern analysis and route recommendations. Additionally, the method may include a feedback loop for drivers to provide information on the effectiveness of the recommendations, allowing for continuous improvement of the system.
[00043]
[00044] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00045] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00046] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00047] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00048] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00049] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A system for intelligent transportation, comprising: a data collection module for collecting traffic information from a plurality of sources; a data processing module for analyzing said traffic information and generating traffic predictions; and a traffic management module for adjusting traffic signals based on said traffic predictions, thereby enhancing traffic flow and reducing congestion.
2. The system of claim 1, further comprising a user interface module for displaying real-time traffic information and recommended routes to drivers.
3. The system of claim 1, wherein said data collection module includes sensors for detecting the presence and movement of vehicles and pedestrians.
4. The system of claim 1, wherein said data processing module uses machine learning algorithms to analyze traffic data and make predictions about traffic patterns and congestion.
5. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals in real-time based on changing traffic conditions and predicted traffic patterns.
6. The system of claim 1, wherein said traffic management module communicates with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level.
7. The system of claim 1, further comprising a feedback module for collecting data on the effectiveness of the traffic management strategies employed by said system, and using said data to refine and improve said strategies over time.
8. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals to prioritize emergency vehicles and public transportation, thereby improving response times and reducing travel times for commuters.
9. A method for enhancing mobility and safety in an intelligent transportation system, comprising: receiving real-time traffic data from a plurality of sources; analyzing said data to identify traffic patterns and potential congestion points; generating route recommendations based on said analysis; and communicating said route recommendations to drivers, thereby optimizing travel time and reducing the risk of accidents.

Intelligent Transportation System to Enhance Mobility and Safety
Abstract
The present invention relates to intelligent transportation systems, and more specifically to systems and methods for enhancing traffic flow, reducing congestion, and improving safety. The system includes a data collection module for gathering traffic information, a data processing module for analyzing traffic data and generating traffic predictions, and a traffic management module for adjusting traffic signals based on the predictions. Additionally, the system includes a user interface module for displaying real-time traffic information and recommended routes to drivers. The system can communicate with other transportation systems to coordinate traffic flow on a regional or national level. The method involves receiving real-time traffic data, analyzing it to identify congestion points and traffic patterns, generating route recommendations, and communicating these recommendations to drivers to optimize travel time and reduce the risk of accidents. The system and method can prioritize emergency vehicles and public transportation, and also includes a feedback module for refining and improving traffic management strategies over time. , Claims:Claims
I/We Claim:
1. A system for intelligent transportation, comprising: a data collection module for collecting traffic information from a plurality of sources; a data processing module for analyzing said traffic information and generating traffic predictions; and a traffic management module for adjusting traffic signals based on said traffic predictions, thereby enhancing traffic flow and reducing congestion.
2. The system of claim 1, further comprising a user interface module for displaying real-time traffic information and recommended routes to drivers.
3. The system of claim 1, wherein said data collection module includes sensors for detecting the presence and movement of vehicles and pedestrians.
4. The system of claim 1, wherein said data processing module uses machine learning algorithms to analyze traffic data and make predictions about traffic patterns and congestion.
5. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals in real-time based on changing traffic conditions and predicted traffic patterns.
6. The system of claim 1, wherein said traffic management module communicates with other transportation systems and devices to coordinate traffic flow and reduce congestion on a regional or national level.
7. The system of claim 1, further comprising a feedback module for collecting data on the effectiveness of the traffic management strategies employed by said system, and using said data to refine and improve said strategies over time.
8. The system of claim 1, wherein said traffic management module is capable of adjusting traffic signals to prioritize emergency vehicles and public transportation, thereby improving response times and reducing travel times for commuters.
9. A method for enhancing mobility and safety in an intelligent transportation system, comprising: receiving real-time traffic data from a plurality of sources; analyzing said data to identify traffic patterns and potential congestion points; generating route recommendations based on said analysis; and communicating said route recommendations to drivers, thereby optimizing travel time and reducing the risk of accidents.