Description:[0019] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0020] Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described. Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.
[0021] The present invention discloses a traffic management system that is capable of managing traffic conditions of a particular area.
[0022] FIG. 1 illustrates a block diagram of a traffic management system (100), according to an embodiment of the present invention.
[0023] In some embodiments, the system (100) comprises a plurality of monitoring modules (102), each equipped with a plurality of sensors (104) and a camera (106). These monitoring modules (102) are installed at various locations throughout the traffic network and are configured to continuously monitor traffic conditions such as vehicle density, speed, and congestion levels.
[0024] Each monitoring module (102) collects real-time data regarding traffic conditions in its respective area. The sensors (104) within each monitoring module (102) capture data on vehicle flow, while the camera (106) provides visual confirmation of road congestion and traffic events. This combination of sensor and camera data enables comprehensive monitoring of traffic conditions across multiple areas of the city.
[0025] The system (100) further comprises a processor (108) communicatively coupled with the monitoring modules (102). The processor (108) is configured to receive and analyze data from the monitoring modules (102). In some embodiments, the processor (108) utilizes advanced algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) models, to process and interpret traffic data efficiently.
[0026] Upon receiving the data, the processor (108) identifies areas of traffic congestion by analyzing patterns such as slowed vehicle speeds, vehicle density, and sudden traffic buildups. Based on this analysis, the processor (108) determines the most congested areas that require immediate traffic regulation to maintain a smooth traffic flow.
[0027] Once areas of congestion are identified, the processor (108) is configured to regulate the operations of traffic lights in those areas. Specifically, the processor (108) adjusts the timing of the traffic lights to alleviate congestion and optimize the flow of vehicles. For example, the processor (108) may increase the green light duration at certain intersections or prioritize specific lanes to reduce waiting times and traffic bottlenecks.
[0028] In one embodiment, the processor (108) continuously updates its traffic light control decisions based on real-time data from the monitoring modules (102). This dynamic traffic management ensures that the system (100) adapts to changing conditions, such as sudden increases in vehicle flow or accidents, to maintain optimal traffic movement.
[0029] The monitoring modules (102) are strategically placed in high-traffic areas, including major intersections, highways, and busy urban streets, to ensure comprehensive coverage of traffic conditions across the entire city. The data collected from these modules provides a detailed overview of the current traffic state, enabling the processor (108) to make precise adjustments to the traffic lights.
[0030] The traffic management system (100) is designed to handle large-scale traffic networks, allowing the processor (108) to manage multiple intersections and congested areas simultaneously. This scalability makes the system (100) suitable for both small urban environments and large metropolitan areas with complex traffic patterns.
[0031] Furthermore, in some embodiments, the processor (108) is capable of learning from historical traffic data to predict future traffic patterns and congestion points. By analyzing trends over time, the system (100) can proactively adjust traffic light operations during peak traffic hours or anticipated traffic events, such as sporting events or public holidays.
[0032] In another embodiment, the system (100) may be integrated with public transportation systems and emergency services, allowing for prioritized control of traffic lights to facilitate faster movement of public buses or emergency vehicles in critical situations.
[0033] By incorporating machine learning models such as CNNs, RNNs, and LSTM networks, the traffic management system enhances its ability to predict traffic patterns and adapt to sudden changes in real-time. This ensures efficient regulation of traffic lights and improved overall traffic flow, reducing congestion and travel times for road users.
[0034] The described traffic management system (100) provides a robust and intelligent solution for controlling and optimizing urban traffic flow. By utilizing real-time data, advanced sensor technologies, and machine learning algorithms, the system (100) helps reduce congestion, improve transportation efficiency, and enhance the overall driving experience for commuters.
[0035] It should be noted that the traffic management system (100) in any case could undergo numerous modifications and variants, all of which are covered by the same innovative concept; moreover, all of the details may be replaced by technically equivalent elements. In practice, the components used, as well as the numbers, shapes, and sizes of the components may be of any kind according to the technical requirements. The scope of protection of the invention is therefore defined by the attached claims. , Claims:WE CLAIM:
1.A traffic management system (100), the system (100) comprising:
a plurality of monitoring modules (102) each having a plurality of sensors (104) and a camera (106), wherein the plurality of monitoring modules (102) are configured to monitor traffic conditions;
a processor (108) communicatively coupled with the plurality of monitoring modules (102), wherein the processor (108) is configured to:
receive data associated with the traffic conditions,
analyse the traffic condition,
determine areas having traffic congestion, and
regulate operations of each traffic light of the areas having traffic congestion to maintain a smooth flow of traffic.

2.The system (100) as claimed in claim 1, wherein the processor (108) comprises a convolution neural networks, recurrent neural networks (RNNs) and long short-test memory (LSTM) models.