Real-time emergency and traffic control system, and method for controlling traffic signals

Abstract

A real-time emergency and traffic control system (100) for controlling traffic signals (104) based on emergency services, is disclosed. The real-time emergency and traffic control system (100) is configured to: (a) obtain first data associated with severity level of first users, and medical centers, from second users, (b) analyze second data associated with source location of the first users, and destination location of medical centers, based on the obtained first data, (c) compute third data associated with number of traffic signals, and real-time traffic occurrences in geo-locations, (d) determine whether emergency vehicles (102) reaches within first threshold distance on at least one side of traffic signals (104), (e) compute first signal in traffic signals (104) being maintained for first time period, and the first signal in the traffic signals (104) being switched from second signal in estimated second time period, (f) identify traffic dense on road when emergency vehicles (102) reaches within second threshold distance, and (g) control traffic signals (104) for adapting emergency vehicles (102) to be passed on the first signal.

Description

[0001] REAL-TIME EMERGENCY AND TRAFFIC CONTROL SYSTEM, AND METHOD FOR CONTROLLING TRAFFIC SIGNALS

[0002] FIELD OF INVENTION

[0003] Embodiments of the present disclosure relate to traffic control based systems and more particularly relate to a real-time emergency and traffic control system, and method for controlling one or more traffic signals based on emergency services.

[0004] BACKGROUND

[0005] Currently, many cities around the world rely on fixed time-based traffic management systems to regulate traffic flow. However, these systems often lead to instances where emergency vehicles, including at least one of: ambulances, fire engines, police vehicles, and the like, get stuck in traffic. Even if other vehicles are willing to give way to the emergency vehicles, their ability to do so is limited. In congested cities including at least one of: Bangalore, Mumbai, Tokyo, and Paris, during peak hours, emergency vehicles may approach a traffic signal that opens up, but the traffic signal can still turn to a red signal due to a fixed timer-based system, requiring the ambulance to wait for the next signal opening. Consequently, the emergency vehicles are forced to wait until the next signal cycle. This cycle repeats itself as the emergency vehicles encounter similar situations at subsequent signals.

[0006] Moreover, if the emergency vehicles are a considerable distance away from a signal, there is a possibility that the traffic controller, who is responsible for manually managing the signal, may not be able to hear a siren or notice the presence of the emergency vehicles. This lack of awareness may result in further delays for the emergency vehicles.

[0007] Once the emergency vehicles (e.g., an ambulance) reach the hospital, there may still be challenges in providing immediate care. A hospital staff at a front desk or an emergency ward needs to be informed about a ground situation and condition of the patient. Hospital authorities then need to assess and prioritize the patients based on a severity level of their cases. This decision-making process can be complex, as the hospital authorities need to balance the urgency of medical attention with the available resources and expertise within the hospital.

[0008] In some cases, the hospital may not have all the necessary resources or specialized expertise, such as in a case of cardiac care. In such situations, the patient may need to be redirected to a nearby hospital that can provide required care. This redirection can further delay the critical care needed by the patient, and the ambulance may face additional challenges in ensuring a smooth transfer of the patient. It is important to note that the decision-making process for prioritizing patients is not always foolproof, and there is a risk of making a wrong call in some cases. This uncertainty adds to the complexity and challenges faced by emergency services and healthcare providers in delivering timely and appropriate care to the patients in critical conditions.

[0009] In certain scenarios, there may arise complex situations where the ambulance is required to be redirected from its intended destination, such as a hospital, to an alternative facility that can better accommodate the specific needs and complexity of the patient's case. This unfortunate circumstance often leads to further challenges for the ambulance, including navigating through traffic congestion and facing uncertainties along the way. Additionally, the ambulance might have encountered a substantial waiting period at the initial hospital before being able to proceed. Furthermore, it is important to indicate that there can be a plurality of ambulances simultaneously traversing the roads, each heading in different directions, while responding to various emergency situations of similar severity level. These situations can occur either within a specific area or throughout an entire city.

[0010] Currently, a pre-defined time -based relay system for traffic management has been implemented to address challenges associated with real-time adjustments of traffic signals. However, the pre-defined time-based relay system does not account for manual inputs by traffic police, which can be cumbersome. In some countries like India, the traffic police may not be able to hear ambulance sirens in noisy traffic from a distance, and a judgment of the traffic police may not always accurately assess length of a queue. As a result, the traffic police often clear one side of a road completely before allowing one or more vehicles from the other side to proceed, inadvertently putting significant stress on the latter. Unfortunately, the pre-defined time-based relay system exacerbates a situation and leads to a chain reaction of worsening congestion. In cases where the emergency services encounter prolonged blockages on one side, the pre-defined time-based relay system further exacerbates the situation for the emergency vehicle and creates a domino effect of worsening conditions on the other side. This exponential worsening of traffic conditions highlights the limitations imposed by human factors in managing such complex situations.

[0011] In some cases, people rely on navigation applications including Google Maps or other similar tools to determine a fastest route to reach the hospital. These applications may only suggest the best available route from options of roads that the navigation applications have access to. The navigation applications do not have the capability to actively control traffic conditions, which means the navigation applications cannot guarantee an effectiveness of a suggested route. Even if an initially recommended route appears to be the best option, the recommended route may still encounter congestion or delays along the way. The traffic conditions are dynamic and can change rapidly, causing stretches of the route to turn red or congested. These navigation applications do not have real-time control over traffic and can only provide information based on available data. Thus, the navigation applications cannot actively manage or optimize traffic flow in real-time.

[0012] The emergency ward at hospitals plays a crucial role in handling critical situations. However, there are instances where people may face challenges when choosing a hospital during a panic situation, especially when time is of the essence. Despite efforts and struggles encountered while navigating through busy roads, there can still be a waiting time once the ambulance reaches the hospital. This waiting time can vary depending on the workload of the hospital and availability of necessary resources. It is not uncommon to witness situations where hospitals redirect ambulance services to another facility (i.e., another hospital). This redirection may occur due to various reasons, such as the lack of specific facilities or expertise needed to address the emergency at hand. Additionally, the non-availability of essential resources, including expert doctors and beds, can also contribute to such redirections. The decision to redirect ambulance services to another hospital is made in the best interest of the patient, aiming to provide the necessary specialized care and resources required for their condition. It ensures that the patient receives optimal treatment and increases the chances of a positive outcome.

[0013] The situation described above indeed adds complexity and can result in the loss of valuable time for the patient. When the ambulance is redirected to another hospital due to the lack of appropriate facilities or expertise, it can potentially delay the prompt medical attention the patient needs. Time is of critical importance in emergency situations, and any delay can have serious consequences. It is unfortunate that in some cases, the hospital is only able to provide basic emergency attention and then request that the patient be transferred to another hospital. This situation can indeed add stress and burden to the patient's family and friends, as well as contribute to additional challenges on the roads.

[0014] Therefore, there is a need for an improved real-time emergency and traffic control system, and method for controlling one or more traffic signals based on emergency services, to address the aforementioned issues.

[0015] SUMMARY

[0016] This summary is provided to introduce a selection of concepts, in a simple manner, which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure.

[0017] In accordance with one embodiment of the disclosure, a real-time emergency and traffic control system for controlling one or more traffic signals based on emergency services, is disclosed. The real-time emergency and traffic control system includes a central server that comprises one or more hardware processors and a memory. The memory is coupled to the one or more hardware processors. The memory comprises a set of program instructions in form of a plurality of subsystems, configured to be executed by the one or more hardware processors. The plurality of subsystems comprises a data obtaining subsystem, a data computing subsystem, a distance determining subsystem, a signal computing subsystem, and a traffic controlling subsystem.

[0018] The data obtaining subsystem is configured to obtain at least one of: historical data and real-time data, associated with at least one of: one or more geo-locations and one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, from one or more external servers. The data obtaining subsystem is further configured to obtain first data associated with at least one of: a severity level of first one or more users, and one or more medical centers for a medical assistance, from one or more user devices of second one or more users.

[0019] The data computing subsystem is configured to analyze second data associated with at least one of: a source location of the first one or more users, and a destination location where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance.

[0020] The data computing subsystem is further configured to compute third data associated with at least one of: a number of the one or more traffic signals between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations, by a machine learning model. The distance determining subsystem is configured to determine whether one or more emergency vehicles reaches within a first threshold distance on at least one side of the one or more traffic signals.

[0021] The signal computing subsystem is configured to compute at least one of: a first signal in the one or more traffic signals being maintained for a first time period, and the first signal in the one or more traffic signals being switched from a second signal in an estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles within the first threshold distance on the at least one side of the one or more traffic signals, by an artificial intelligence model.

[0022] The distance determining subsystem is further configured to determine whether the one or more emergency vehicles reaches within a second threshold distance on the at least one side of the one or more traffic signals.

[0023] The traffic controlling subsystem is configured to identify a traffic dense on a road in the at least one side of the one or more traffic signals when the one or more emergency vehicles reaches within the second threshold distance on the at least one side of the one or more traffic signals. The traffic controlling subsystem is further configured to control the one or more traffic signals for adapting the one or more emergency vehicles to be passed on the first signal through the at least one side of the one or more traffic signals, based on the traffic dense identified on the road in the at least one side of the one or more traffic signals.

[0024] In an embodiment, in analyzing the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, the data computing subsystem is configured to: (a) obtain the first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance, from the data obtaining subsystem, (b) compare the obtained first data associated with the one or more medical centers, with the historical data associated with the one or more geo-locations, and (c) analyze the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, based on the comparison of the obtained first data associated with at least one of: the severity level of the first one or more users and the one or more medical centers for the medical assistance, with the historical data associated with the one or more geo-locations.

[0025] In another embodiment, in computing, by the machine learning model, the third data associated with at least one of: the number of the one or more traffic signals between the source location and the destination location, and the real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, the data computing subsystem is configured to: (a) obtain the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, (b) compare the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, with the historical data, and (c) compute the third data associated with at least one of: the number of the one or more traffic signals between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on the comparison of the second data associated with the source location of the first one or more users and the destination location where the one or more medical centers is located, with the historical data.

[0026] In yet another embodiment, the distance determining subsystem is further configured to determine whether the one or more emergency vehicles reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals based on at least one of: the one or more geo-locations of the second one or more users determined by a global positioning system configured in the one or more user devices of the second one or more users, and the global positioning system configured in the one or more emergency vehicles. In yet another embodiment, the distance determining subsystem is further configured to determine whether the one or more emergency vehicles reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals based on one or more internet of things (loT) devices configured in the one or more emergency vehicles.

[0027] In yet another embodiment, the traffic controlling subsystem is configured to identify the traffic dense on the road in the at least one side of the one or more traffic signals based on at least one of: one or more vision-based sensors and one or more camera devices, configured in the one or more traffic signals, when the one or more emergency vehicles reaches within the second threshold distance on the at least one side of the one or more traffic signals.

[0028] In yet another embodiment, the traffic controlling subsystem is configured to provide priority to the one or more emergency vehicles for adapting the one or more emergency vehicles to travel through the at least one side of the one or more traffic signals based on proximity of the one or more emergency vehicles to the one or more traffic signals.

[0029] In yet another embodiment, the central server is configured to transmit the first data associated with the severity level of the first one or more users to a computing system of the one or more medical centers for providing a treatment to the first one or more users.

[0030] In yet another embodiment, the computing system of the one or more medical centers is configured to: (a) analyze whether one or more medical assistances comprising at least one of: medical facilities and bed facilities, is available for providing the treatment to the first one or more users upon obtaining the first data associated with the severity level of the first one or more users from the central server and (b) transmit information associated with second one or more medical centers, to the central server when the one or more medical assistances comprising at least one of: the medical facilities and the bed facilities, is lack in the one or more medical centers for providing the treatment to the first one or more users. In yet another embodiment, the central server is configured to indicate one or more signals to third one or more users travelling in second one or more sides of the one or more traffic signals, and wherein the one or more signals indicates information associated with travelling of the one or more emergency vehicles on the at least one side of the one or more traffic signals.

[0031] In one aspect, a computer-implemented method for controlling one or more traffic signals based on emergency services, is disclosed. The computer-implemented method includes obtaining, by one or more hardware processors, at least one of: historical data and real-time data, associated with at least one of: one or more geolocations and one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, from one or more external servers.

[0032] The computer-implemented method further includes obtaining, by the one or more hardware processors, first data associated with at least one of: a severity level of first one or more users, and one or more medical centers for a medical assistance, from one or more user devices of second one or more users.

[0033] The computer-implemented method further includes analyzing, by the one or more hardware processors, second data associated with at least one of: a source location of the first one or more users, and a destination location where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance.

[0034] The computer-implemented method further includes computing, by the one or more hardware processors, third data associated with at least one of: a number of the one or more traffic signals between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geolocations, by a machine learning model. The computer-implemented method further includes determining, by the one or more hardware processors, whether one or more emergency vehicles reaches within a first threshold distance on at least one side of the one or more traffic signals.

[0035] The computer-implemented method further includes computing, by the one or more hardware processors, at least one of: a first signal in the one or more traffic signals being maintained for a first time period, and the first signal in the one or more traffic signals being switched from a second signal in an estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles within the first threshold distance on the at least one side of the one or more traffic signals, by an artificial intelligence model.

[0036] The computer-implemented method further includes determining, by the one or more hardware processors, whether the one or more emergency vehicles reaches within a second threshold distance on the at least one side of the one or more traffic signals.

[0037] The computer- implemented method further includes identifying, by the one or more hardware processors, a traffic dense on a road in the at least one side of the one or more traffic signals when the one or more emergency vehicles reaches within the second threshold distance on the at least one side of the one or more traffic signals.

[0038] The computer- implemented method further includes controlling, by the one or more hardware processors, the one or more traffic signals for adapting the one or more emergency vehicles to be passed on the first signal through the at least one side of the one or more traffic signals, based on the traffic dense identified on the road in the at least one side of the one or more traffic signals.

[0039] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

[0040] BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0042] FIG. 1 is a block diagram of a real-time emergency and traffic control system for controlling one or more traffic signals based on emergency services, in accordance with an embodiment of the present disclosure;

[0043] FIG. 2 is a detailed view of a central server of the real-time emergency and traffic control system, such as those shown in FIG. 1, in accordance with an embodiment of the present disclosure;

[0044] FIG. 3 is a block diagram depicting an overview of the real-time emergency and traffic control system for controlling the one or more traffic signals based on the emergency services, such as those shown in FIG. 1, in accordance with an embodiment of the present disclosure;

[0045] FIG. 4 is an exemplary view of the one or more traffic signals controlled by the central server based on the emergency services, in accordance with an embodiment of the present disclosure; and

[0046] FIG. 5 is a flow chart depicting a computer-implemented method for controlling the one or more traffic signals based on the emergency services, in accordance with an embodiment of the present disclosure.

[0047] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

[0048] DETAILED DESCRIPTION

[0049] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

[0050] 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 a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by “comprises... a” does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting. In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

[0052] A computer system (standalone, client or server computer system) configured by an application may constitute a “module” (or “subsystem”) that is configured and operated to perform certain operations. In one embodiment, the “module” or “subsystem” may be implemented mechanically or electronically, so a module include dedicated circuitry or logic that is permanently configured (within a specialpurpose processor) to perform certain operations. In another embodiment, a “module” or “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.

[0053] Accordingly, the term “module” or “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and / or to perform certain operations described herein.

[0054] FIG. 1 is a block diagram of a real-time emergency and traffic control system 100 for controlling one or more traffic signals 104 based on emergency services, in accordance with an embodiment of the present disclosure. The real-time emergency and traffic control system 100 controls / manages a real-time traffic based on the one or more emergency vehicles 102 (i.e., emergency vehicles 102 utilized for emergency services) approaching on roads. The one or more emergency vehicles 102 may include at least one of: fire brigade vehicles, police vehicles, ambulances, security squad, and the like. The real-time emergency and traffic control system 100 overtakes its previous commands associated with the one or more traffic signals 104 and control the one or more traffic signals 104 in such a way that the one or more emergency vehicles 102 get priority to pass through the one or more traffic signals 104 during traffic time. The real-time emergency and traffic control system 100 controls each traffic signal of the one or more traffic signals 104 until the one or more emergency vehicles 102 reaches its destination location. The real-time emergency and traffic control system 100 is more synchronized and harmonized way so that the one or more emergency vehicles 102 need not to wait at the one or more traffic signals 104.

[0055] The real-time emergency and traffic control system 100 includes a central server 106 that is configured to control the one or more traffic signals 104 based on the one or more emergency vehicles 102. In an embodiment, the central server 106 is communicatively connected to the one or more traffic signals 104 and the one or more emergency vehicles 102 through a communication network 110. In an embodiment, the communication network 110 may be at least one of: a Wireless- Fidelity (Wi-Fi) connection, a hotspot connection, a Bluetooth connection, a local area network (LAN), a wide area network (WAN), any other wireless network, and the like. In an embodiment, the central server 106 may be a cloud-based central server.

[0056] The real-time emergency and traffic control system 100 is initially configured to obtain at least one of: historical data and real-time data, associated with at least one of: one or more geo-locations and one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, from one or more external servers. The real-time emergency and traffic control system 100 is further configured to obtain first data associated with at least one of: a severity level of first one or more users, and one or more medical centers (i.e., one or more hospitals) for a medical assistance (i.e., a medical treatment), from one or more user devices of second one or more users.

[0057] In an embodiment, the one or more users may be one or more patients who is in emergency. In an embodiment, the second one or more users may include at least one of: one or more ambulance staffs, family members, friends, and helping people, of the one or more patients. In an embodiment, the one or more user devices may include at least one of: a mobile phone, a Smartphone, a personal computer, a laptop, an electronic notebook, and the like.

[0058] The real-time emergency and traffic control system 100 is further configured to analyze second data associated with at least one of: a source location of the first one or more users, and a destination location where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance.

[0059] The real-time emergency and traffic control system 100 is further configured to compute third data associated with at least one of: a number of the one or more traffic signals 104 between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geolocations and the one or more traffic occurrences in the one or more geo-locations, by a machine learning model.

[0060] The real-time emergency and traffic control system 100 is further configured to determine whether the one or more emergency vehicles 102 reaches within a first threshold distance on at least one side (e.g., on east side) of the one or more traffic signals 104. In an embodiment, the first threshold distance may be five hundred meters distance from the one or more traffic signals 104. The real-time emergency and traffic control system 100 is further configured to compute at least one of: a first signal in the one or more traffic signals 104 being maintained for a first time period, and the first signal in the one or more traffic signals 104 being switched from a second signal in an estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles 102 within the first threshold distance on the at least one side of the one or more traffic signals 104, by an artificial intelligence (Al) model. The real-time emergency and traffic control system 100 is further configured to determine whether the one or more emergency vehicles 102 reaches within a second threshold distance on the at least one side (e.g., on the east side) of the one or more traffic signals 104. In an embodiment, the second threshold distance may be three hundred meters distance from the one or more traffic signals 104. The real-time emergency and traffic control system 100 is further configured to identify a traffic dense on a road in the at least one side of the one or more traffic signals 104 when the one or more emergency vehicles 102 reaches within the second threshold distance on the at least one side of the one or more traffic signals 104. The real-time emergency and traffic control system 100 is further configured to control the one or more traffic signals 104 for adapting the one or more emergency vehicles 102 to be passed on the first signal through the at least one side of the one or more traffic signals 104, based on the traffic dense identified on the road in the at least one side of the one or more traffic signals 104.

[0061] The real-time emergency and traffic control system 100 is further configured to transmit the first data associated with the severity level of the first one or more users to a computing system 112 of the one or more medical centers for providing a treatment to the first one or more users.

[0062] The computing system 112 of the one or more medical centers is configured to analyze whether one or more medical assistances including at least one of: medical facilities and bed facilities, is available for providing the treatment to the first one or more users upon obtaining the first data associated with the severity level of the first one or more users from the real-time emergency and traffic control system 100. The computing system 112 of the one or more medical centers is further configured to transmit information associated with second one or more medical centers, to the real-time emergency and traffic control system 100 when the one or more medical assistances comprising at least one of: the medical facilities and the bed facilities, is lack in the one or more medical centers for providing the treatment to the first one or more users. FIG. 2 is a detailed view of the central server 106 of the real-time emergency and traffic control system 100, such as those shown in FIG. 1, in accordance with an embodiment of the present disclosure. The central server 106 includes one or more hardware processor(s) 218. The central server 106 further includes a memory 202 coupled to the one or more hardware processor(s) 218. The memory 202 includes a set of program instructions in form of the plurality of subsystems 108.

[0063] The one or more hardware processor(s) 218, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

[0064] The memory 202 includes the plurality of subsystems 108 stored in the form of executable program which instructs the one or more hardware processor(s) 218 via a system bus 214 to perform the above-mentioned method steps. The plurality of subsystems 108 includes following subsystems: a data obtaining subsystem 204, a data computing subsystem 206, a distance determining subsystem 208, a signal computing subsystem 210, and a traffic controlling subsystem 212.

[0065] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electronically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the one or more hardware processor(s) 218. The plurality of subsystems 108 includes the data obtaining subsystem 204 that is communicatively connected to the one or more hardware processor(s) 218. The data obtaining subsystem 204 is configured to obtain at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations for the one or more time periods of each day, from the one or more external servers. In an embodiment, the one or more external servers pre-stores the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations.

[0066] The data obtaining subsystem 204 is further configured to obtain the first data associated with at least one of: the severity level of the first one or more users and the one or more medical centers for the medical assistance, from the one or more user devices of the second one or more users. In an embodiment, the second one or more users (e.g., ambulance staff or family members of the patient) inputs the first data associated with details of the severity level of the patient and the one or more medical centers on their one or more user devices. In an embodiment, inputting of the first data associated with the severity level details of the first one or more users, by the second one or more users, is mandatory.

[0067] In an embodiment, the severity levels of the first one or more users are pre-defined in the central server 106. For example, the second one or more users input severity level as 3 being lower priority like possible fracture of the first one or more users who is able to speak and stable but must be taken to the medical center for treatment. In another example, the one or more users who has mild heart attack to be inputted as severity level 1 with red dot on an application for visual selection with short description of “very high priority”. In other words, a priority access with the priority level of 1 which is automatically chosen by the central server 106 and not available to decide by the second one or more users (i.e., the ambulance staff) as deciding priority in the central server 106 is based on the input from the one or more user devices through the application. When the one or more emergency vehicles 102 is started from the source location, the application with the data associated with the registered one or more emergency vehicles 102 recommends the one or more medical centers in vicinity based on at least one of: distance, present traffic in between the source location and the destination location, speciality and availability of emergency services in the one or more medical centers upon the first one or more users (i.e., the ambulance staff) provides information associated with possibles causes (e.g., accident, unconscious of the second one or more users, bleeding from the one or more users, burning, the severity of the second one or more users, and the like), based on the Al model implemented in the central server 106.

[0068] The Al model performs data analytics and smart features to help the first one or more users for reaching the one or more medical centers on time to save life of the first one or more users. In an embodiment, the application may be further configured to notify an emergency desktop of the one or more medical centers through push notifications in advance when the information is entered by the second one or more users. The application may further inform an estimated time of arrival of the one or more emergency vehicles 102 to the emergency desktop of the one or more medical centers.

[0069] In another embodiment, the application may be configured to receive messages from the one or more medical centers. The application is further configured to perform smart communication and guidance to the second one or more users (i.e., the ambulance staff and family members of the patient) who is having the application. In an embodiment, the ambulance staff may have an option to override suggestions and to select the one or more medical centers based on the best instinct or attached hospital network to an organization of the one or more emergency vehicles 102. In an embodiment, the application may include options / solutions for other emergency services that may get benefited in events of critical need.

[0070] The plurality of subsystems 108 further includes the data computing subsystem 206 that is communicatively connected to the one or more hardware processor(s) 218. The data computing subsystem 206 is configured to analyze the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance. In an embodiment, the source location may be at least one of: a patient’s home, a road side where an accident has occurred, a location where fire accidents are occurred, and the like).

[0071] In an embodiment, for analyzing the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, the data computing subsystem 206 is configured to obtain the first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance, from the data obtaining subsystem 204. The data computing subsystem 206 is further configured to compare the obtained first data associated with the one or more medical centers, with the historical data associated with the one or more geo-locations.

[0072] The data computing subsystem 206 is further configured to analyze the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, based on the comparison of the obtained first data associated with at least one of: the severity level of the first one or more users and the one or more medical centers for the medical assistance, with the historical data associated with the one or more geolocations.

[0073] The data computing subsystem 206 is further configured to compute the third data associated with at least one of: the number of the one or more traffic signals 104 between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on at least one of: the historical data and the real- time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations, by the machine learning model.

[0074] In an embodiment, for computing the third data associated with at least one of: the number of the one or more traffic signals 104 between the source location and the destination location, and the real-time traffic occurrences in the one or more geolocations between the source location and the destination location, the data computing subsystem 206 is configured to obtain the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located.

[0075] The data computing subsystem 206 is further configured to compare the second data associated with at least one of: the source location of the first one or more users, and the destination location where the one or more medical centers is located, with the historical data.

[0076] The data computing subsystem 206 is further configured to compute the third data associated with at least one of: the number of the one or more traffic signals 104 between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, based on the comparison of the second data associated with the source location of the first one or more users and the destination location where the one or more medical centers is located, with the historical data, by the machine learning model. In an embodiment, the machine learning model may include at least one of: a linear regression model, a decision tree model, and random forest model, and the like.

[0077] The plurality of subsystems 108 further includes the distance determining subsystem 208 that is communicatively connected to the one or more hardware processor(s) 218. The distance determining subsystem 208 is configured to determine whether the one or more emergency vehicles 102 reaches within the first threshold distance on at least one side of the one or more traffic signals 104. The plurality of subsystems 108 further includes the signal computing subsystem 210 that is communicatively connected to the one or more hardware processor(s) 218. The signal computing subsystem 210 is configured to compute at least one of: the first signal (e.g., a green signal) in the one or more traffic signals 104 being maintained for the first time period, and the first signal in the one or more traffic signals 104 being switched from the second signal (e.g., a red signal) in the estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles 102 within the first threshold distance on the at least one side of the one or more traffic signals 104, by the artificial intelligence model.

[0078] The distance determining subsystem 208 is further configured to determine whether the one or more emergency vehicles 102 reaches within the second threshold distance on the at least one side of the one or more traffic signals 104. In an embodiment, the distance determining subsystem 208 is configured to determine whether the one or more emergency vehicles 102 reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals 104 based on at least one of: the one or more geolocations of the second one or more users determined by a global positioning system (GPS) configured in the one or more user devices of the second one or more users, and the global positioning system configured in the one or more emergency vehicles 102.

[0079] In an embodiment, the distance determining subsystem 208 is configured to determine whether the one or more emergency vehicles 102 reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals 104 based on radio frequency identification (RFID) tag configured in the one or more emergency vehicles 102.

[0080] In another embodiment, the distance determining subsystem 208 is further configured to determine whether the one or more emergency vehicles 102 reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals 104 based on one or more internet of things (loT) devices configured in the one or more emergency vehicles 102.

[0081] The plurality of subsystems 108 further includes the traffic controlling subsystem 212 that is communicatively connected to the one or more hardware processor(s) 218. The traffic controlling subsystem 212 is configured to identify the traffic dense on the road in the at least one side of the one or more traffic signals 104 when the one or more emergency vehicles 102 reaches within the second threshold distance on the at least one side of the one or more traffic signals 104. The traffic controlling subsystem 212 is further configured to control the one or more traffic signals 104 for allowing the one or more emergency vehicles 102 to be passed on the first signal through the at least one side of the one or more traffic signals 104, based on the traffic dense identified on the road in the at least one side of the one or more traffic signals 104.

[0082] In an embodiment, the traffic controlling subsystem 212 is configured to identify the traffic dense on the road in the at least one side of the one or more traffic signals 104 based on at least one of: one or more vision-based sensors and one or more camera devices, configured in the one or more traffic signals 104, when the one or more emergency vehicles 102 reaches within the second threshold distance on the at least one side of the one or more traffic signals 104.

[0083] In an embodiment, the Al model may update overall traffic logic to make a normal traffic so that the one or more emergency vehicles 102 has less impact and needs to overrule the one or more traffic signals 104 in case of emergency is minimal, when at least one of: the historic data associated with traffic pattern, emergency loads at the one or more medical centers, road conditions / dense around 1 kilometer, 2 kilometers and 3 kilometers radius (if needed more). Further, the Al model recommends one or more authorities of city / municipal corporation to determine ways to add one or more satellite emergency wards to ensure primary treatment are available between the source location and the destination location. The one or more satellite emergency wards is available in rare situations but may be needed in some locations between the source location and the destination location.

[0084] The Al model is configured to obtain the data (e.g., historic data and current data) to analyze the traffic patterns and to adapt the real-time emergency and traffic control system 100 to make decisions for reducing traffic so that the one or more emergency vehicles 102 have less stresses during providing the emergency services. The historic data and current data may include information associated with at least one of: the traffic patterns in busy hours of all direction at a particular area, a traffic post, conditions of narrow roads in forward directions during non-busy hours, information about new metro project, alternate road developments happening in that particular area, possibilities of changing of traffic situations for at least three to five years, a number a speciality hospitals in the particular area and their access roads from a radius of three to five kilometers, commercial offices, IT parks, industrial and residential developments, schooling infrastructure happening in the particular area, expectation of population migration to a city in half of a decade, and the like.

[0085] The Al model is utilized the above historic and current data from road conditions and is configured to analyze hypothesis of possible rate of increase of commuters by at least one of: utilizing the historic and current data for improving a situation around for now and near period, and analyzing future plans through a plurality of prediction models, regression analysis, and simulation models. The AL model is configured to recommend the best possible ways to mitigate risks of high traffic situations.

[0086] As an initial step, the Al model analyzes the data and identifies the traffic patterns to provide quick and short remedies on reducing the traffic in the particular area. All the data from the traffic post and area are mapped to the Al model that runs on algorithms to make decisions on possible logics and test the data through the simulation that exactly represents the traffic patterns based on moving average of time, location, direction, frequency of emergency services (i.e., the one or more emergency vehicles 102) posing through the area. Finally, the Al model determines the best possible ways to reduce / ease the traffic. Further, in real-time, one or more machines is configured to interact with each other and to collaborate together with the traffic posts. The one or more machines have constant communications with each other and also with the one or more emergency vehicle 102, other emergency systems, the one or more medical centers, and one or more users (e.g., the first and second one or more users) of the real-time emergency and traffic control system 100.

[0087] In an embodiment, the traffic controlling subsystem 212 is further configured to provide priority to the one or more emergency vehicles 102 for adapting the one or more emergency vehicles 102 to travel through the at least one side of the one or more traffic signals 104 based on proximity of the one or more emergency vehicles 102 to the one or more traffic signals 104. For example, a first emergency vehicle is coming from east direction approaching the one or more traffic signals 104. A second emergency vehicle is coming from north direction approaching the same one or more traffic signals 104. In that scenario, the central server 106 analyzes the severity level of the first one or more users based on the first data obtained from the second one or more users of the first and second emergency vehicles. The central server 106 then allows either the first emergency vehicle or the second emergency vehicle based on the severity level of the one or more users travelling in the emergency vehicles 102.

[0088] In another example, when the first two or more users travelling in the first emergency vehicle and the second emergency vehicle have same severity level of emergency, then the central server 106 computes the proximity of the first and second emergency vehicles to the one or more traffic signals 104. The emergency vehicle with closest distance from the one or more traffic signals 104 is allowed to pass through the one or more traffic signals 104.

[0089] In an embodiment, the central server 106 is further configured to transmit the first data associated with the severity level of the first one or more users to the computing system 112 of the one or more medical centers for providing the treatment to the first one or more users.

[0090] The computing system 112 of the one or more medical centers is configured to analyze whether the one or more medical assistances including at least one of: the medical facilities and the bed facilities, is available for providing the treatment to the first one or more users upon obtaining the first data associated with the severity level of the first one or more users from the central server 106. The computing system 112 of the one or more medical centers is further configured to transmit information associated with the second one or more medical centers, to the central server 106 when the one or more medical assistances including at least one of: the medical facilities and the bed facilities, is unavailable in the one or more medical centers for providing the treatment to the first one or more users.

[0091] In an embodiment, the one or more medical centers are configured to receive and redirect the one or more emergency vehicles 102 based on availability of at least one of: physicians, beds, and the like, in the one or more medical centers. The data associated with the availability of at least one of: the physicians, the beds, and the like, are mapped to the data of the real-time emergency and traffic control system 100 for instructing the one or more emergency vehicles 102 to the one or more medical centers having all the availabilities of at least one of: the physicians, the beds, and the like. In an embodiment, the instruction to the one or more emergency vehicles 102 is a real-time command / instruction and layer two as original command / instruction of recommending the one or more medical centers is based on overall database and a part of location services.

[0092] In an embodiment, the central server 106 is further configured to indicate one or more signals to third one or more users travelling in second one or more sides of the one or more traffic signals 104. In an embodiment, the one or more signals indicates information associated with travelling of the one or more emergency vehicles 102 on the at least one side of the one or more traffic signals 104. FIG. 3 is a block diagram 300 depicting an overview of the real-time emergency and traffic control system 100 for controlling the one or more traffic signals 104 based on the emergency services, such as those shown in FIG. 1, in accordance with an embodiment of the present disclosure. As shown in FIG. 3, the central server 106 may be configured in at least one of: a traffic control tower, and a dedicated server room, as shown in block 302. In an embodiment, the central server 106 may be controlled by a technical team with at least one of: one or more traffic police and one or more personnels who controls the traffic. The first data associated with the severity level of the emergency of the first one or more first users and the one or more medical centers, are transmitted to the central server 106, as shown in block 304.

[0093] The one or more hardware elements including at least one of: the one or more vision-based sensors and the one or more camera devices (shown in FIG. 4) configured in a traffic post, the RFID tag, a programmable logic controller (PLC), supervisory control and data acquisition (SCAD A), open platform communications united architecture (OPCUA), the GPS, fifth generation (5G) enabled devices, AI / ML model, firmware, and the like, for identifying the traffic dense on the road in the at least one side of the one or more traffic signals 104. The information associated with the traffic dense on the road in the at least one side of the one or more traffic signals 104, are transmitted to the central server 106, as shown in block 306.

[0094] The global positioning system (GPS) configured in the one or more user devices of the second one or more users, the global positioning system configured in the one or more emergency vehicles 102, and the Bluetooth configured in the one or more emergency vehicles 102, are used for the central server 106 to determine whether the one or more emergency vehicles 102 reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals 104, as shown in block 308. The central server 106 is configured to utilize the machine learning model and the artificial intelligence model for (a) computing the third data associated with at least one of: the number of the one or more traffic signals 104 between the source location and the destination location, and one or more real-time traffic occurrences in the one or more geo-locations between the source location and the destination location, and (b) computing at least one of: the first signal (e.g., a green signal) in the one or more traffic signals 104 being maintained for the first time period, and the first signal in the one or more traffic signals 104 being switched from the second signal (e.g., a red signal) in the estimated second time period, respectively, as shown in block 310.

[0095] The central server 106 is configured to transmit control information to the traffic post to control the one or more traffic signals 104 based on the one or more emergency service vehicles 102 approaching the one or more traffic signals 104, as shown in block 312.

[0096] FIG. 4 is an exemplary view 400 of the one or more traffic signals 104 controlled by the central server 106 based on the emergency services, in accordance with an embodiment of the present disclosure. FIG. 4 describes a case study of controlling the one or more traffic signals 104 based on the emergency services.

[0097] Unfortunately, in a residential colony, one of the families had a situation of the first one or more users (e.g., the patient) with age of around 60, who had suffered by a mild heart attack and the family of the first one or more users need to quickly visit to a hospital before more complications faced by the patient. The family of the first one or more users call ambulance services. The family of the first one or more users decide to visit a cardiac care facility hospital in their area which is around six kilometers from their home (i.e., the source location 402). However, the family of the one or more users are new to a city so that the family call the ambulance services (i.e., the one or more emergency vehicles 102). Due to the prompt response of the emergency vehicle 102 (i.e., the ambulance), the second one or more users (e.g., the ambulance staff of the ambulance) made a firm commitment to arrive at the patient's home within a span of 5 minutes. The family of the first one or more users did not call the one or more medical centers (i.e., the hospital).

[0098] As a next step, the second one or more users log on the application in the one or more user devices to the best cardiac care hospital (e.g., XYZ hospital) in the area which is towards west of their home and the cardiac care hospital (i.e., the destination location 404) is six kilometers distance from their home. It is mandatory for the second one or more users (e.g., the ambulance staff) to quick log while the first one or more users (e.g., the patient) is getting prepared to the ambulance by family members. The corresponding cardiac care hospital receives an intimation of severity level 1 that is logged through the computing system 112, from the second one or more users.

[0099] The corresponding cardiac care hospital receives the intimation of the severity level 1 because there may be one or more cases at emergency ward and there is possibilities for the patient to be wait. The medical personnel of the emergency ward need to inform on a duty doctor who may be at ward visit, there could be no facility at the hospital of over occupied. Hence, the hospital can redirect / recommend the ambulance to determine another hospital facility (e.g., ABC hospital) with right specificities in the area. This saves time and eliminates all delay and possible frustration as this situation is nobody’s fault but stress levels are very high during such emergencies.

[0100] The emergency vehicle 102 (i.e., the ambulance) is started from the source location 402, and the emergency vehicle 102 needs to get to a main road and thereafter the emergency vehicle 102 has a traffic signal 104 (e.g., XXX signal) within seven hundred meters. The traffic signal 104 has four roads crossing each other at a roundabout with the traffic signal 104. Further, the traffic signal 104 is a busy signal normally and the emergency vehicle 102 has to cross this traffic signal 104 around 6 PM in evening.

[0101] The second one or more users of the one or more emergency vehicles 102 logged the emergency as severity level 1 and the real-time emergency and traffic control system 100 considers the emergency as priority 1 and the real-time emergency and traffic control system 100 immediately starts analyzing the source location 402 and the destination location 404 based on the inputs (i.e., the first data) associated with the geolocation and the severity level of the first one or more users (i.e., the patient), obtained from the second one or more users (i.e., the ambulance staff).

[0102] The real-time emergency and traffic control system 100 computes the number of the one or more traffic signals 104 between the source location 402 and the destination location 404, and one or more real-time traffic occurrences in the one or more geo-locations between the source location 402 and the destination location 404, based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations, by the machine learning model.

[0103] The real-time emergency and traffic control system 100 determines whether the emergency vehicle 102 reaches within the first threshold distance and the real-time emergency and traffic control system 100 still computes if the first signal is going to green in next thirty to forty five seconds and in this scenario the real-time emergency and traffic control system 100 will still wait and does not trigger a special action. In an embodiment, the first threshold distance may be five hundred meters from the traffic signal 104.

[0104] The real-time emergency and traffic control system 100 determines whether the emergency vehicle 102 reaches within the second threshold distance. In an embodiment, the second threshold distance may be three hundred meters from the traffic signal 104. When the emergency vehicle 102 reaches three hundred meters distance from the traffic signal 104, the real-time emergency and traffic control system 100 is at more active level. The real-time emergency and traffic control system 100 analyzes entire situation and determines traffic dense using the one or more vision-based sensors and one or more camera devices 406. Based on the traffic dense determined on the road in the traffic signal 104, the real-time emergency and traffic control system 100 allows the emergency vehicle 102 to pass in the first signal path. In an embodiment, the real-time emergency and traffic control system 100 determines the distance of the emergency vehicle 102 based on sensors attached to an internet of things (loT) device configured in the emergency vehicle 102, which helps to communicate with the emergency vehicle 102 within four hundred meters to maximum of five hundred meters with a unique tag for each emergency vehicle 102. The real-time emergency and traffic control system 100 provides no command to the traffic signal 104 for the special action based on the AI / ML model when the traffic is less, and the signal is turned to green as the emergency vehicle 102 is reached within three hundred meters of proximity to the traffic signal 104.

[0105] In some instance, when a slow-moving bulkier vehicle is in front of the emergency vehicle 102 and the slow-moving bulkier vehicle may put the emergency vehicle 102 is danger. In such situation, if the emergency vehicle 102 is two hundred meters away, the real-time emergency and traffic control system 100 will wait till last ten seconds and even then, if the emergency vehicle 102 is not within fifty to hundred meters, then the real-time emergency and traffic control system 100 will kick off special action and overtake signal logic from previously used basis data analytics of the traffic signal 104 (i.e., the XXX signal) and fixed time for the traffic signal 104. In an embodiment, the signal logic in this present invention is not same as fixed relay-based signal logic. The signal logic in the present invention improvises normal traffic basis of data analytics of Al model, ML model and firmware, hardware in loop. In order to facilitate the smooth passage of the emergency vehicle 102, the special action is taken at the traffic signal 104. The green signal status for incoming traffic from the east side is extended until the emergency vehicle 102 has safely crossed the intersection. Following this, the signal will transition from yellow to red.

[0106] After passing the traffic signal 104 (i.e., XXX signal), the emergency vehicle 102 will encounter a second traffic signal (i.e., YYY signal) within a distance of approximately one kilometer. Under normal circumstances, this signal is synchronized with the first traffic signal 104 to accommodate the incoming flow of traffic from the north side. However, in emergency situations like the one described in this case study, the priority will shift to facilitating the movement of vehicles from the east to the west. Regardless of the direction from which the emergency vehicle 102 is approaching the hospital, the traffic signal 104 will prioritize clearing the path for the emergency vehicle 102. The same logic employed at the traffic signal 104 will be applied at subsequent signals. This coordinated approach will significantly expedite the emergency vehicle’s 102 journey, allowing emergency vehicle 102 to reach the destination location 404 (i.e., the hospital) much faster than the current solution.

[0107] In a second scenario, as shown in FIG. 4, when the emergency vehicle 102 crosses a busy four direction traffic signal post (e.g., from east to west) with additional thirty seconds taken to clear the emergency vehicle 102. In this case, the traffic signal provides more traffic on south, north and west side as the traffic signal took additional thirty seconds. Once, the emergency vehicle 102 has crossed east to west side road, west to east (including south / north branches from west side flow) traffic signals may get eight seconds more to clear additional accumulated traffic. If yes, based on the historical data analysis mapped for the traffic signal post and time taken on normal course when the eight seconds is not sufficient. Further, the eight seconds is not granted if the traffic is less from west to at least one of: east, north and south sides outflow need. Further, the additional eight seconds may be provided in times where s a subject side of traffic signal is impacted (i.e., west to east / north / south), based on the Al model configured in the central server 106. The Al model in the central server computes the traffic signals based on a situation and defined logic steps.

[0108] Similarly, rest of the two sides may get additional ten and fourteen seconds respectively on logic based on rea-time need basis. In an embodiment, the allocation of additional timings may be done in one or more times until the situation is brought to normal (i.e., the traffic signal post which has similar traffic flow from all sides and is most busy traffic post at least during rush hours). In an embodiment, smart traffic signal posts in a direction of the one or more medical centers are normally synchronized in such a way that the traffic signal post has smooth flow as very first (e.g., when the emergency vehicle 102 moves from east to west at the major traffic post, a very next traffic post may be opened based on distance and time calculations of allowable average speed in such a way normal traffic clears one signal and then the traffic signal post may get other also opened as the emergency vehicle 102 reaches the next traffic signal post and so on. In this way, the emergency vehicle 102 including at least one of: the ambulance, the fire brigade, and the like passes through all such critical routes.

[0109] In an embodiment, the smart traffic post (i.e., a first traffic post) may include a light detection and ranging (LIDAR) system in a busy area. The smart traffic post may further include at least one computing device taking decisions at a local area in case there is at least one of: a business case and a primary / secondary decision making protocol. The computing device in the smart traffic post is configured to communicate with a next smart traffic post (i.e., a second traffic post) about occurrences of the traffic in real-time at the smart traffic post and send an alert to the next smart traffic post before such need for the next smart traffic post, using the Al model.

[0110] In an embodiment, the AI / ML model can be integrated / configured in the one or more emergency vehicles 102. The AI / ML model in the one or more emergency vehicles 102 helps to provide two way communication between the smart traffic post and the emergency vehicle 102. In an embodiment, the one or more emergency vehicles 102 with the AL / ML model may be vehicles used for emergency services and specially licensed vehicles as part of the real-time emergency and traffic control system 100. The one or more emergency vehicles 102 having the AI / ML model is configured to provide decisions on the traffic signals and to recommend the ambulance staff to take right and smart decisions (i.e., which path to select and constantly interact with the smart traffic post during action times). For an instance, four sides of roads, at a particular traffic signal, are engaged with a heavy traffic and each side of the roads has ninety seconds of fixed time relay in a conventional traffic post. The real-time emergency and traffic control system 100 determines the emergency vehicle 102 that is coming from east to west side and stuck around five hundred meters. The real-time emergency and traffic control system 100 provides priority to the east to west side, which makes traffic on all other sides on more stress. But the real-time emergency and traffic control system 100 intelligently reduces time relay on other sides to thirty seconds and clears the traffic in the east to west side in the time duration of thirty seconds.

[0111] In case, the real-time emergency and traffic control system 100 takes too much time to clear the traffic in the east to west side, then the real-time emergency and traffic control system 100 brings the emergency vehicle 102 close to two hundred meters and switch the signal red for the side (i.e., for not two hundred seventy seconds, which is the sum of ninety seconds of other three sides). The real-time emergency and traffic control system 100 is configured to adjust sequence such a way that one of the sides is opened for thirty seconds and the real-time emergency and traffic control system 100 focuses on the side of the emergency vehicle 102 to clear the emergency vehicle 102 (i.e., the emergency vehicle 102 has to wait another thirty seconds at each side of poll wanting to go west side). The real-time emergency and traffic control system 100 is configured to control the traffic upon passing of the emergency vehicle 102 through the traffic signal to ensure that the traffic pattern is not disrupted too much in the traffic signal.

[0112] In another instance, in a situation before implementing the real-time emergency and traffic control system 100, a traffic post includes busy traffic in rush hours from all four directions and each direction includes ninety seconds release time in clockwise rotation. Initially, a preconfigured program recommends the traffic post to follow what exactly is happening in terms of a logic in case of a conventional fixed relay system. The ML model configured in the real-time emergency and traffic control system 100 is configured to collect the data to make a better decision on the traffic post. The ML model is further configured to send the data to the database where the collected data is processed to determine alternate ways (i.e., two opposite directions are released in sequence so the chowing on one side of the traffic post released of Green signal can be eased). The ML model being a part of the Al model helps to determine the possible ways in finding the traffic patterns from the historic and current data.

[0113] In case the one or more emergency vehicles 102 approaches the traffic post, the ML model is configured to compare the data with the pre-defined data to provide the best ways for reducing the traffic. The ML model is configured to utilize the data and provides the best ways until the one or more emergency vehicles reaches the destination location 404. This coordination of the ML model works in real-time based on a feedback and tracking of the one or more emergency vehicles in realtime. There are some scenarios given below, in which the ML model is configured.

[0114] If an emergency vehicle 102 is approaching a traffic signal from east to west and the emergency vehicle 102 passes without any extra efforts. The emergency vehicle 102 waits till ten seconds before the emergency vehicle 102 reaches at least fifty to seventy five meters and the real-time emergency and traffic control system 100 does not perform any special actions as the emergency vehicle 102 passes the traffic signal. However, If the emergency vehicle 102 is little far so that the ML model in the real-time emergency and traffic control system 100 may extend the time duration till the emergency vehicle 102 crosses the traffic signal and then may try compensated in later loops so all sides are again balanced to current levels. The best part is that the emergency vehicle 102 does not need to wait and post crossing of the traffic signal, the ML model obtains inputs from the vision system (e.g., LIDAR based inputs) to check whether there is a need of compensation adjustments to other sides. In an embodiment, the ML model is pre-programed and hence called as a supervised / reinforcement machine learning.

[0115] So, if the emergency vehicle 102 takes extra ten seconds then the ML model may not alter any further proceedings in terms of ninety seconds each to all next traffic directions, but the ML model has to stop already progressing with green side from any of rest three sides. If the emergency vehicle 102 reaches in pre-programed decision making distance and the ML model must stop irrespective of any status of green signal harshly with yellow warning to one of non-priority side. The ML model determines whether the emergency vehicle 102 takes priority and computes how much extra time to be allocated to each of the sides as lender and reduces some time from east to west side in next cycle (e.g., when no emergency services are crossing right after in next turn). So, the ML model with the use of an exponential algorithm to compute timings and to divide them amongst next three sides.

[0116] In an embodiment, the ML model may be at least one of: reinforcement learning and a supervised learning. The reinforcement learning is based on the data initially obtained and the AI / ML model takes a call on traffic management solutions. While in real-time for the one or more emergency vehicles 102 to drive through challenging situations, the ML model helps to provide priority to the one or more emergency vehicles 102 based on predefined algorithms. In an embodiment, an unsupervised learning may be used to make decisions based on a maturity of the ML model.

[0117] FIG. 5 is a flow chart depicting a computer-implemented method 500 for controlling the one or more traffic signals 104 based on the emergency services, in accordance with an embodiment of the present disclosure. At step 502, at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, are obtained from one or more external servers.

[0118] At step 504, the first data associated with at least one of: the severity of the first one or more users, and the one or more medical centers for a medical assistance, are obtained from one or more user devices of second one or more users.

[0119] At step 506, the second data associated with at least one of: the source location 402 of the first one or more users, and the destination location 404 where the one or more medical centers is located, are analyzed based on the obtained first data associated with at least one of: the severity of the first one or more users, and the one or more medical centers for the medical assistance.

[0120] At step 508, the third data associated with at least one of: a number of the one or more traffic signals 104 between the source location 402 and the destination location 404, and one or more real-time traffic occurrences in the one or more geolocations between the source location 402 and the destination location 404, are computed based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations, by the machine learning model.

[0121] At step 510, it determines whether the one or more emergency vehicles 102 reaches within the first threshold distance on at least one side of the one or more traffic signals 104.

[0122] At step 512, at least one of: the first signal in the one or more traffic signals 104 being maintained for the first time period, and the first signal in the one or more traffic signals 104 being switched from the second signal in the estimated second time period, is computed based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles 102 within the first threshold distance on the at least one side of the one or more traffic signals 104, by the artificial intelligence model.

[0123] At step 514, it determines whether the one or more emergency vehicles 102 reaches within a second threshold distance on the at least one side of the one or more traffic signals 104.

[0124] At step 516, the traffic dense on the road in the at least one side of the one or more traffic signals 104 is identified when the one or more emergency vehicles 102 reaches within the second threshold distance on the at least one side of the one or more traffic signals 104. At step 518, the one or more traffic signals 104 is controlled for adapting the one or more emergency vehicles 102 to be passed on the first signal through the at least one side of the one or more traffic signals 104, based on the traffic dense identified on the road in the at least one side of the one or more traffic signals 104.

[0125] The present invention has following advantages. The present invention provides real-time priority services for emergency needs to save lives. The present invention provides the real-time emergency and traffic control system 100 for an active traffic management and for keeping the hospitals ready with prior information for handling emergency.

[0126] The present invention is configured to help in emergency situations including fire, security forces to reach at gunshot crime location using the real-time emergency and traffic control system 100. The present invention works much smarter and better even in today’s situation and the present invention needs not much time and infrastructure. The present invention is configured to provide real-time monitoring and action based on a need of priority and to manage the traffic smartly. The realtime emergency and traffic control system 100 of the present invention is more synchronized and harmonized way so that the one or more emergency vehicles 102 need not to wait at the one or more traffic signals 104.

[0127] The present invention includes the real-time emergency and traffic control system 100 that includes the one or more hardware elements. The present invention will work seamlessly because of the one or more hardware elements. The real-time emergency and traffic control system 100 has a backup plan for primary looping (i.e., if one hardware element fails other hardware element works in the real-time emergency and traffic control system 100). If all the hardware elements fail then the real-time emergency and traffic control system 100 utilizes a conventional system to control the one or more traffic signals 104.

[0128] The real-time emergency and traffic control system 100 can be communicated to at least one of: the emergency vehicles 102 (i.e., at least one of: the ambulance, the fire brigade, police / security vehicles, and the like), the application (i.e., a mobile application), the one or more medical centers, emergency service offices, highway patrol locations, toll collection booths, and components of public infrastructure, and the like, for a noble purpose of saving lives. The above said components are well integrated so that the whole real-time emergency and traffic control system 100 takes maximum benefits.

[0129] In case of accidents, an emergency save our souls (SOS) sends information to the one or more user devices. In an embodiment, an emergency SOS alert system is integrated in one or more vehicles of users. The emergency SOS alert system is communicatively connected to the real-time emergency and traffic control system 100 so that the real-time emergency and traffic control system 100 provides alerts / notifications to the one or more emergency vehicles to reach a spot of at least one of: the accident and fire incidents.

[0130] The present invention with the real-time emergency and traffic control system 100 sends one or more commends to activate an old fixed time based relay system until all security calls are properly verified and restored for restart (i.e., may be applied to a specific area / locality / sector or whole city based on the severity of glitch), when an event of at least one of: software failure, hardware failure, hijacking of a three layer security system of the real-time emergency and traffic control system 100.

[0131] Further, the present invention with the real-time emergency and traffic control system 100 includes an automatic monitoring and punishment system configured to generate bills based on one or more evidences using local traffic rules. Further, the automatic monitoring and punishment system transmits data associated with the generated bills with the one or more evidences to at least one of: an auto generated governing system and one or more authorities, of the one or more traffic signals 104, for taking necessary actions.

[0132] The present invention with the real-time emergency and traffic control system 100 is configured to share the data associated with the traffic computations and intelligences with directors of town and country planning (DTCP) for development of city expansions and development of new towns so the data associated with the traffic computations and intelligences are used to clear the emergency services in the traffic. The data associated with the traffic computations and intelligences can also be shared with other countries where development of city and town planning is underway, and this sharing of the data can help at mass scale to learn from others specially during development phases. For example, tire-II and tire-III cities are developing very fast in India. Since, there is a lack of accurate of data analysis, same mistakes are happening during development of new towns and development on extension part of cities.

[0133] The present invention with the real-time emergency and traffic control system 100 helps one or more navigation applications including at least one of: Google, TOMTOM, Apple maps, and the like. The real-time emergency and traffic control system 100 is configured to share real-time alert / indication to one or more commuters and to help the one or more commuters to determine the best route. The real-time emergency and traffic control system 100 is configured to indicate approaching of the one or more emergency vehicles 102 to the one or more commuters so that the one or more commuters can do small adjustments to give way to the one or more emergency vehicles 102. In normal scenarios, the one or more commuters move left from median to give the way to the one or more emergency vehicles 102. However, the real-time emergency and traffic control system 100 provides the indication to the one or more commuters of the moving vehicles in advance so that the one or more commuters are better prepared to give the way to the one or more emergency vehicles 102.

[0134] The real-time emergency and traffic control system 100 can be used by the one or more authorities associated with the one or more traffic signals 104 when higher officials including at least one of: governors, ministers, security services, and the like needing priority and the real-time emergency and traffic control system 100 is seamless in providing the priority to the emergency services. The real-time emergency and traffic control system 100 is configured to communicate / co-ordinate with the one or more emergency vehicles 102 including at least one of: the ambulance, the specially licensed vehicles, police vehicles, government high profile leaders squads, and the like, at a potential conflicting zone, to help an overall ecosystem to operate smoothly.

[0135] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

[0136] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, and the like. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer- usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, an apparatus, or a device.

[0137] The medium can be an electronic, a magnetic, an optical, an electromagnetic, an infrared, or a semiconductor system (or an apparatus or a device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, a magnetic tape, a removable computer diskette, a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include a compact disk-read only memory (CD-ROM), a compact disk-read / write (CD-R / W) and a DVD.

[0138] Input / output (VO) devices (including but not limited to keyboards, displays, pointing devices, and the like.) can be coupled to the real-time emergency and traffic control system 100 either directly or through intervening VO controllers. Network adapters may also be coupled to the real-time emergency and traffic control system 100 to enable a data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

[0139] A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling / real-time emergency and traffic control system 100 in accordance with the embodiments herein. The realtime emergency and traffic control system 100 herein comprises at least one of: a processor or a central processing unit (CPU). The CPUs are interconnected via the system bus 214 to various devices such as a random-access memory (RAM), readonly memory (ROM), and an input / output (I / O) adapter. The I / O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the real-time emergency and traffic control system 100. The real-time emergency and traffic control system 100 can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.

[0140] The real-time emergency and traffic control system 100 further includes a user interface adapter that connects a keyboard, a mouse, a speaker, a microphone, and / or other user interface devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, a printer, or a transmitter, for example.

[0141] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device / article may be used in place of the more than one device or article, or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the invention need not include the device itself.

[0142] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, and the like. Of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms 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.

[0143] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Claims

WE CLAIM:

1. A real-time emergency and traffic control system (100) for controlling one or more traffic signals (104) based on emergency services, the real-time emergency and traffic control system (100) comprising: a central server (106) comprising: one or more hardware processors (218); and a memory (202) coupled to the one or more hardware processors (218), wherein the memory (202) comprises a set of program instructions in form of a plurality of subsystems (108), configured to be executed by the one or more hardware processors (218), wherein the plurality of subsystems (108) comprises: a data obtaining subsystem (204) configured to: obtain at least one of: historical data and real-time data, associated with at least one of: one or more geolocations and one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, from one or more external servers; obtain first data associated with at least one of: a severity level of first one or more users, and one or more medical centers for a medical assistance, from one or more user devices of second one or more users; a data computing subsystem (206) configured to: analyze second data associated with at least one of: a source location (402) of the first one or more users, and a destination location (404) where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance;compute third data associated with at least one of: a number of the one or more traffic signals (104) between the source location (402) and the destination location (404), and one or more real-time traffic occurrences in the one or more geo-locations between the source location (402) and the destination location (404), based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geo-locations, by a machine learning model; a distance determining subsystem (208) configured to determine whether one or more emergency vehicles (102) reaches within a first threshold distance on at least one side of the one or more traffic signals (104); a signal computing subsystem (210) configured to compute at least one of: a first signal in the one or more traffic signals (104) being maintained for a first time period, and the first signal in the one or more traffic signals (104) being switched from a second signal in an estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles (102) within the first threshold distance on the at least one side of the one or more traffic signals (104), by an artificial intelligence model; the distance determining subsystem (208) further configured to determine whether the one or more emergency vehicles (102) reaches within a second threshold distance on the at least one side of the one or more traffic signals (104); and a traffic controlling subsystem (212) configured to: identify a traffic dense on a road in the at least one side of the one or more traffic signals (104) when the one or more emergency vehicles (102) reaches within the secondthreshold distance on the at least one side of the one or more traffic signals (104); and control the one or more traffic signals (104) for adapting the one or more emergency vehicles (102) to be passed on the first signal through the at least one side of the one or more traffic signals (104), based on the traffic dense identified on the road in the at least one side of the one or more traffic signals (104).

2. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein in analyzing the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located, the data computing subsystem (206) is configured to: obtain the first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance, from the data obtaining subsystem (204); compare the obtained first data associated with the one or more medical centers, with the historical data associated with the one or more geo-locations; and analyze the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located, based on the comparison of the obtained first data associated with at least one of: the severity level of the first one or more users and the one or more medical centers for the medical assistance, with the historical data associated with the one or more geo-locations.

3. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein in computing, by the machine learning model, the third data associated with at least one of: the number of the one or more traffic signals (104) between the source location (402) and the destination location (404), and the realtime traffic occurrences in the one or more geo-locations between the sourcelocation (402) and the destination location (404), the data computing subsystem (206) is configured to: obtain the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located; compare the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located, with the historical data; and compute the third data associated with at least one of: the number of the one or more traffic signals (104) between the source location (402) and the destination location (404), and one or more real-time traffic occurrences in the one or more geo-locations between the source location (402) and the destination location (404), based on the comparison of the second data associated with the source location (402) of the first one or more users and the destination location (404) where the one or more medical centers is located, with the historical data.

4. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the distance determining subsystem (208) is further configured to determine whether the one or more emergency vehicles (102) reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals (104) based on at least one of: the one or more geo-locations of the second one or more users determined by a global positioning system configured in the one or more user devices of the second one or more users, and the global positioning system configured in the one or more emergency vehicles (102).

5. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the distance determining subsystem (208) is further configured to determine whether the one or more emergency vehicles (102) reaches within at least one of: the first threshold distance and the second threshold distance, on the at leastone side of the one or more traffic signals (104) based on one or more internet of things (loT) devices configured in the one or more emergency vehicles (102).

6. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the traffic controlling subsystem (212) is configured to identify the traffic dense on the road in the at least one side of the one or more traffic signals (104) based on at least one of: one or more vision-based sensors and one or more camera devices (406), configured in the one or more traffic signals (104), when the one or more emergency vehicles (102) reaches within the second threshold distance on the at least one side of the one or more traffic signals (104).

7. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the traffic controlling subsystem (212) is configured to provide priority to the one or more emergency vehicles (102) for adapting the one or more emergency vehicles (102) to travel through the at least one side of the one or more traffic signals (104) based on proximity of the one or more emergency vehicles (102) to the one or more traffic signals (104).

8. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the central server (106) is configured to transmit the first data associated with the severity level of the first one or more users to a computing system (112) of the one or more medical centers for providing a treatment to the first one or more users.

9. The real-time emergency and traffic control system (100) as claimed in claim 8, wherein the computing system (112) of the one or more medical centers is configured to: analyze whether one or more medical assistances comprising at least one of: medical facilities and bed facilities, is available for providing the treatment to the first one or more users upon obtaining the first data associated with the severity level of the first one or more users from the central server (106); andtransmit information associated with second one or more medical centers, to the central server (106) when the one or more medical assistances comprising at least one of: the medical facilities and the bed facilities, is lack in the one or more medical centers for providing the treatment to the first one or more users.

10. The real-time emergency and traffic control system (100) as claimed in claim 1, wherein the central server (106) is configured to indicate one or more signals to third one or more users travelling in second one or more sides of the one or more traffic signals (104), and wherein the one or more signals indicates information associated with travelling of the one or more emergency vehicles (102) on the at least one side of the one or more traffic signals (104).

11. A computer-implemented method (500) for controlling one or more traffic signals (104) based on emergency services, the computer- implemented method (500) comprising: obtaining (502), by one or more hardware processors (218), at least one of: historical data and real-time data, associated with at least one of: one or more geolocations and one or more traffic occurrences in the one or more geo-locations for one or more time periods of each day, from one or more external servers; obtaining (504), by the one or more hardware processors (218), first data associated with at least one of: a severity level of first one or more users, and one or more medical centers for a medical assistance, from one or more user devices of second one or more users; analyzing (506), by the one or more hardware processors (218), second data associated with at least one of: a source location (402) of the first one or more users, and a destination location (404) where the one or more medical centers is located, based on the obtained first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance; computing (508), by the one or more hardware processors (218), third data associated with at least one of: a number of the one or more traffic signals (104)between the source location (402) and the destination location (404), and one or more real-time traffic occurrences in the one or more geo-locations between the source location (402) and the destination location (404), based on at least one of: the historical data and the real-time data, associated with at least one of: the one or more geo-locations and the one or more traffic occurrences in the one or more geolocations, by a machine learning model; determining (510), by the one or more hardware processors (218), whether one or more emergency vehicles (102) reaches within a first threshold distance on at least one side of the one or more traffic signals (104); computing (512), by the one or more hardware processors (218), at least one of: a first signal in the one or more traffic signals (104) being maintained for a first time period, and the first signal in the one or more traffic signals (104) being switched from a second signal in an estimated second time period, based on at least one of: the real-time data associated with the traffic occurrences and reaching of the one or more emergency vehicles (102) within the first threshold distance on the at least one side of the one or more traffic signals (104), by an artificial intelligence model; determining (514), by the one or more hardware processors (218), whether the one or more emergency vehicles (102) reaches within a second threshold distance on the at least one side of the one or more traffic signals (104); identifying (516), by the one or more hardware processors (218), a traffic dense on a road in the at least one side of the one or more traffic signals (104) when the one or more emergency vehicles (102) reaches within the second threshold distance on the at least one side of the one or more traffic signals (104); and controlling (518), by the one or more hardware processors (218), the one or more traffic signals (104) for adapting the one or more emergency vehicles (102) to be passed on the first signal through the at least one side of the one or more traffic signals (104), based on the traffic dense identified on the road in the at least one side of the one or more traffic signals (104).

12. The computer-implemented method (500) as claimed in claim 11, wherein analyzing (506) the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located, comprising: obtaining, by the one or more hardware processors (218), the first data associated with at least one of: the severity level of the first one or more users, and the one or more medical centers for the medical assistance, from the data obtaining subsystem (204); comparing, by the one or more hardware processors (218), the obtained first data associated with the one or more medical centers, with the historical data associated with the one or more geo-locations; and analyzing, by the one or more hardware processors (218), the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located, based on the comparison of the obtained first data associated with at least one of: the severity level of the first one or more users and the one or more medical centers for the medical assistance, with the historical data associated with the one or more geo-locations.

13. The computer-implemented method (500) as claimed in claim 11, wherein computing (508), by the machine learning model, the third data associated with at least one of: the number of the one or more traffic signals (104) between the source location (402) and the destination location (404), and the real-time traffic occurrences in the one or more geo-locations between the source location (402) and the destination location (404), comprising: obtaining, by the one or more hardware processors (218), the second data associated with at least one of: the source location (402) of the first one or more users, and the destination location (404) where the one or more medical centers is located; comparing, by the one or more hardware processors (218), the second data associated with at least one of: the source location (402) of the first one or moreusers, and the destination location (404) where the one or more medical centers is located, with the historical data; and computing, by the one or more hardware processors (218), the third data associated with at least one of: the number of the one or more traffic signals (104) between the source location (402) and the destination location (404), and one or more real-time traffic occurrences in the one or more geo-locations between the source location (402) and the destination location (404), based on the comparison of the second data associated with the source location (402) of the first one or more users and the destination location (404) where the one or more medical centers is located, with the historical data.

14. The computer- implemented method (500) as claimed in claim 11, further comprising determining, by the one or more hardware processors (218), whether the one or more emergency vehicles (102) reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals (104) based on at least one of: the one or more geolocations of the second one or more users determined by a global positioning system configured in the one or more user devices of the second one or more users, and the global positioning system configured in the one or more emergency vehicles (102).

15. The computer- implemented method (500) as claimed in claim 11, further comprising determining, by the one or more hardware processors (218), whether the one or more emergency vehicles (102) reaches within at least one of: the first threshold distance and the second threshold distance, on the at least one side of the one or more traffic signals (104) based on one or more internet of things (loT) devices configured in the one or more emergency vehicles (102).

16. The computer-implemented method (500) as claimed in claim 11, wherein the traffic dense on the road in the at least one side of the one or more traffic signals (104), is identified based on at least one of: one or more vision-based sensors and one or more camera devices (406), configured in the one or more traffic signals(104), when the one or more emergency vehicles (102) reaches within the second threshold distance on the at least one side of the one or more traffic signals (104).

17. The computer-implemented method (500) as claimed in claim 11, further comprising providing, by the one or more hardware processors (218), priority to the one or more emergency vehicles (102) for adapting the one or more emergency vehicles (102) to travel through the at least one side of the one or more traffic signals (104) based on proximity of the one or more emergency vehicles (102) to the one or more traffic signals (104).

18. The computer- implemented method (500) as claimed in claim 11, further comprising transmitting, by the one or more hardware processors (218), the first data associated with the severity level of the first one or more users to a computing system (112) of the one or more medical centers for providing a treatment to the first one or more users.

19. The computer-implemented method (500) as claimed in claim 18, further comprising: analyzing, by the computing system (112) of the one or more medical centers, whether one or more medical assistances comprising at least one of: medical facilities and bed facilities, is available for providing the treatment to the first one or more users upon obtaining the first data associated with the severity level of the first one or more users from the central server (106); and transmitting, by the computing system (112) of the one or more medical centers, information associated with second one or more medical centers, to the central server (106) when the one or more medical assistances comprising at least one of: the medical facilities and the bed facilities, is lack in the one or more medical centers for providing the treatment to the first one or more users.

20. The computer- implemented method as claimed in claim 11, further comprising indicating, by the one or more hardware processors (218), one or moresignals to third one or more users travelling in second one or more sides of the one or more traffic signals (104), and wherein the one or more signals indicates information associated with travelling of the one or more emergency vehicles (102) on the at least one side of the one or more traffic signals (104).

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