FIELD OF THE INVENTION

The present invention relates to a traffic signal system, particularly an optimized traffic signal system for better traffic management.

BACKGROUND OF THE INVENTION

In urban and metropolitan cities around the world, the number of vehicles per capita is high and it is still growing. This is due to populations concentrated in industrial hubs and the need for commuting long distances within the city networks. Also, a private vehicle is considered as a luxury due to the personal comfort that it provides to the individual. In such metropolitan hubs, there are a lot of public transport services including buses, cabs, etc., that aid the individuals to commute to and from their workplaces. Thus, these public and private vehicles contribute to the vehicular traffic moving through the cities.

In order to manage the vehicular activity through the city networks, the traffic signals play an important role in the management of traffic. The signals enable congestion free movement of the traffic in a disciplined and regulated fashion. Today, India faces several challenges with traffic signals, which contribute to traffic congestion and delays, which in turn contribute to a higher carbon footprint to the environment, and many safety concerns.

Many traffic signals have improper timing or are not synchronized effectively which causes long queues of vehicles at intersections, causing traffic congestion and delays. Accordingly, inefficient signal timing also increases fuel consumption and air pollution by vehicles. Further, traffic signal violations are also common, largely due to inadequate enforcement. Motorists often ignore red lights, leading to chaotic traffic situations and increased risk of accidents. Strict governance is necessary to ensure compliance with traffic signals, but many a times that is not sufficient. Some traffic signals lack proper infrastructure, such as clear signage, signal lights, and road markings that may confuse drivers and pedestrians, leading to accidents and inefficiencies in traffic flow.

Mostly traffic signals do not adequately cater to the needs of pedestrians. Lack of proper sidewalks, pedestrian crossings, and signal timings for pedestrians may result in unsafe conditions. It is crucial to design signals and infrastructure that prioritize pedestrian safety and convenience. The implementation of advanced technologies and smart traffic management systems is limited in many cities. These systems, including adaptive signal control and real-time monitoring, may optimize signal timings based on traffic conditions and help alleviate congestion. But considering the high-cost solutions, it becomes unrealistic to adopt such advanced systems in most of the cities.

Since we have very limited smart traffic management systems, we majorly rely on traffic controlling police and their staff. Any manual intervention is error prone considering operations are continuous, monotonous and human fatigue. Lack of awareness among the public about the importance of following traffic signals contributes to the problem. Effective campaigns and educational programs are needed to promote traffic rules, signal compliance, and responsible driving behavior. There have been many improvements in the traffic signals to cater to the traffic management. Some of the attempts in the prior art are discussed below. Chinese Patent Application No. CN101916512A to Shaojie Ying describes an intelligent traffic signal lamp that can display different colors, patterns, symbols, characters and digits by point- set display technology. These colors, patterns and symbols are customized according to different applications for improved traffic management. The invention provides an intelligent traffic signal lamp, which is technically capable of replacing the conventional traffic signal lamp completely and is developed newly.

Another, Korean Patent Application No. KR20100101487A to Park Jae Young discloses a system for controlling traffic signal using a smart card to allow an emergency vehicle to pass an intersection. A system for controlling a traffic signal lamp using a smart card is provided to allow an emergency vehicle to pass an intersection by controlling a traffic signal in response to a request from the emergency vehicle.

The Chinese Patent Application. CN112885119A to Shanghai Tuli Information Technology Co., Ltd. is a Traffic intersection unmanned signal control real-time optimization method. The Chinese Patent Application includes a Web terminal and a cloud server that aids in real time monitoring of traffic and adjusts the traffic signals to avoid congestion. Through the cooperation of the modules, the real-time road condition can be checked after the equipment terminal logs in the system; the system can sense the urban intersection traffic lights in real time and performs intelligent unmanned control on the traffic lights through intelligent sensing, data fusion and model analysis. The systems known in the art do not cater to the traffic management in real time to solve the problem of traffic congestion during peak hours in the urban hubs. Also, for passing emergency vehicles, specialized systems like the smart cards are required. The systems known in the art also do not provide a real time solution to vehicle breakdown, water logging, accident, tree collapse and other such incidents that require real time monitoring for traffic management. The systems known in the art are complex in their operation and require ample monetary inputs for smooth and efficient functioning.

There is a need for an optimized traffic signal management system that monitors the traffic and aids the traffic management in real time.

SUMMARY OF THE INVENTION:

A traffic signal management system for controlling traffic signal according to the traffic situations on the roads including a memory chip configured for storing data that includes a primary control unit that is removably integrated with the traffic signal for capturing stationary and/or dynamic objects along roads being recorded by a plurality of sensors which are positioned on traffic signals. A controller is configured for processing data received from the sensors, and a communication module to communicate with a secondary control unit. The secondary control unit processes data through an event hub module which is received from the primary control unit. Further, a processing module is configured in the secondary control unit to analyze and process the image data received from the primary control unit. Also ann image processing module of the secondary control unit is present to identify moving and/or stationary object and process real time images that control the signals for opening and closing roads in accordance with the real time traffic conditions.

The traffic signal management system of the present invention includes the first module that is configured to process the image data and identify the traffic congestion using artificial intelligence and object detection. The second module is trained to identify the emergency vehicles arriving on a particular road through a machine learning module. The third module is trained by the artificial intelligence module to identify the natural calamities. Further, the secondary control unit trains machine learning module based on processed images to resolve traffic congestion. The second module identifies special vehicles to activate appropriate traffic signals to open or close roads. The third module 256 is configured to identify natural calamity situations and activate the traffic signals.

The traffic signal management system further includes a traffic data consumer module that is configured to analyze the data in the blob storage that is received by the machine learning module. The traffic data consumer module also includes traffic object density, frequency, time -based trends, passive analysis, active analysis, image analysis, sound data analysis, weather analysis, noise analysis. The Cosmos DB 228 stores the unprocessed data in the present invention.

The traffic signal management system also includes a dynamic module that is configured to identify the relative motion between any two objects captured in the images. It is to be noted that the primary control unit is removably integrable with the existing traffic signal systems. Further, the image processing module identify relative distances between two objects, relative velocity between two objects, approximate size and volume of the object. This image processing module is configured to group the objects based on speed, size and relative distance between the objects.

The dynamic module of the present invention is configured to identify the relative motion between any two objects captured in the images. The traffic signal management system also includes an environment module that is configured for receiving sound/noise levels and air pollution levels.

RIEF DESCRIPTION OF THE DRAWINGS

The objectives and advantages of the present invention will become apparent from the following description read in accordance with the accompanying drawings wherein

FIG. 1 shows a traffic signal management system 100 integrated with existing traffic light systems 108;

FIG. 2 shows a schematic of the traffic signal management system 100 of FIG. 1; and

FIG. 3 shows an operational flow of the image processing module of FIG.l in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION The invention described herein is explained using specific exemplary details for better understanding. However, the invention disclosed can be worked on by a person skilled in the art without the use of these specific details.

References in the specification to "one embodiment" or "an embodiment" means that particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

References in the specification to “preferred embodiment” means that a particular feature, structure, characteristic or function described in detail thereby omitting known constructions and functions for clear description of the present invention.

Now referring to FIG. 1, a traffic signal management system 100 in accordance with a preferred embodiment of the present invention is described. The traffic signal management system 100 referred to as ‘the system 100’ is installed and integrable with a new or a previously installed traffic light system 108. The traffic signal management system 100 of the present invention is integrable with the traffic light systems 108 that are known in the prior art. The system 100 includes a primary control unit 104 and a secondary control unit 106.

The primary control unit 104 receives, processes and manages the data received from traffic light system 108 and its surrounding. In accordance with the present invention, the range of the sensors 124 is changed as per the requirement and parameters like traffic density, weather conditions, pollution levels etc. The secondary control unit 106 analyzes the data received from primary control unit 104 and provides optimized results for controlling traffic signal system 108. It is noted, however, that at times the traffic light system 108 is already installed at various locations, for example, a square, an intersection, or a crossing 112 on roads 116.

The traffic light systems 108 generally includes a number of indicator lights including red light, green light and yellow light that are selectively illuminated to manage the traffic flow. The traffic light systems 108 also include indicator arrows to guide the vehicles with the possible directions in which the vehicles may proceed. The traffic light systems 108 has countdown timers to display the amount of time left for the indicator light to change the color and accordingly direct the vehicular movement.

The system 100 includes a primary control unit 104 and a secondary control unit 106 that is preferably advantageously remotely located at a predefined location. The primary control unit 104 is integrated with respective traffic light systems 108. The primary control unit 104 is also connected with the secondary control unit 106 preferably by communication mediums such as internet, intranet or wired network. It is noted that the system 100 in other embodiments includes several primary control units depending upon the routes, the traffic signals and the traffic congestion along the particular road. For example, in one embodiment the route is selected on where there are four traffic signal systems 100. Hence, in this embodiment, four identical primary control units 104 will be integrably installed with each of the four traffic signals 108. However, this embodiment includes only one secondary control unit 106 that communicates with all four primary control units 104.

Each of the primary control units 104 includes sensors 124, a controller 128, a memory chip 132, and a communication module 136. The sensors 124 include cameras, sound sensor, temperature sensor, water sensor etc. However, for the purpose of brevity the cameras, sound sensor, temperature sensor, water sensor etc. are collectively referred as camera/s. The real time data received from the traffic signal system 108 includes parameters such as Boolean values of the lights illuminating in a traffic signal for example, red light is on so green light is off and the like.

The sensors 124 capture images of the traffic in all directions around the primary control unit 104. In this present embodiment the type of sensors 124 are red light cameras, Automatic Number Plate Recognition (ANPR) cameras, Speed Camera or Radar Based Camera. The sensors 124 capture real time data of the traffic and send it to the controller 128. Further, the real time data is processed by the controller 128. In accordance with the present invention, the real time data captures ‘traffic density’, ‘emergency vehicle’, ‘vehicles violating traffic rules’ and the like. The communication module 136 receives the processed data from the controller 128 and sends the real time data to the secondary control unit 106.

The communication module 136 includes a GPS Module, a Data Transfer Module, and an Internet Module. The GPS module identifies the location of the primary control unit 104 i.e., the location of the respective traffic management system 100 in a predefined space, for example, a demographic, locality, city, or a territory on the earth. The Data Transfer Module communicates the transfer or exchange of the data between the primary control unit 104 and the secondary control unit 106. The internet module connects the primary controller unit 104 with the secondary control unit 106. The communication module 136 is configured to locate the traffic management system 100, communicate the data between the primary and the secondary control units 104, 106 and then connect both of the primary and the secondary control units 104, 106 with the internet or any intranet.

The controller 128 processes and analyzes the real time data and communicates with the secondary control unit 106 to provide instructions to the primary control unit 104. Accordingly, the lights in the traffic management system 100 are ignited in accordance with the traffic density on the roads around the traffic light system 108.

Now referring to FIG. 2 and 3, a schematic of the secondary control unit 106 in accordance with the present invention is described. The secondary control unit 106 includes an IOT hub module 200, an event hub module 204, data bricks 208, and system analytics module 212. The data captured by traffic signals is sent via the IOT hub module 200 to the event hub module 204. Accordingly, the event hub module 204 gets the data streamed preferably on a cloud or a remote server. The stream analysis module 212 performs real time analysis of data received from primary control unit 104 located at various traffic signals and gives input to the device 104 to control the lights in the traffic signal system as per the real time analysis.

The secondary control unit 106 also includes a backup services module 216, a blob storage 220, a traffic data consumer module 224, and a Cosmos DB 228. The backup service module 216 is configured to record and initiate the process of backup of data at predefined times as desired or as per inputs. The traffic data consumer module 224 is configured to analyze the data in the blob storage 220 that is received by the machine learning module 236. The analysis includes traffic object density, frequency, time based trends, passive analysis, active analysis, image analysis, sound data analysis, weather analysis, noise analysis. The blob storage 220 defines a backup storage. The Cosmos DB 228 is raw data store that stores unprocessed data.

The streamed data is sent to the raw data store 228 wherein the raw data from the primary control unit 104 is stored. It is noted that the raw data store 228 is configured and is expired after a predefined time interval. The streamed data is pushed to backup storage 220 wherein the raw data from the primary control unit 104 is stored such that this data remains for longer duration till the data is archived. The data bricks 208 execute the data processing and include inbuilt intelligence for image processing of real time data in the data bricks 208.

The secondary control unit 106 also includes image processing module 232, ML module 236 and reference data module 240. The image processing module 232 performs real time processing of the images. The image processing module 232 is configured to identify the predefined objects from the images captured and stored in the data base such as blob storage 220 or any other data base. The image processing module 232 processes and does identification of the objects, for example, vehicles, bicycles, bikes, individuals, any other moving or stationary objects. After identification of the objects in accordance with the method of the present invention discussed below, the image processing module 232 obtains time to be allotted to start or stop a traffic signal along a particular road. In accordance with the present invention, the image processing module 232 identifies the objects along all roads in the vicinity of the sensors 124 and identifies the time to be allotted to keep signal open or close along that particular road. The secondary control unit 106 trains ML module 236 based on data obtained by image processing module 232.

Now the steps involved in operation of the image processing module 232 to obtain time to be allotted for a particular portion of road by the traffic signal 108. In an initial step 305, the image processing module 232 identifies length and breadth of a particular road i.e., identification of a stationary parameter. In a next step 310, dynamic parameters related to the traffic situations are identified. In a next step 315, the image processing module 232 identifies two or more images captured by the sensors 124 between two time intervals for example, images taken at 2 seconds, 5 seconds, 10 seconds interval, along the same road for a predefined distance.

In a next step 320, the image processing module 232 identifies relative distance between two objects, relative velocity between two objects and the approximate size and volume of the object. In a next step 325, the atmospheric parameters relative to the surrounding such as temperature, moisture and the water on the road are gathered from the sensors 124. In this step, the pollution control level and the noise control level and the air quality index are received from the sensors 124. The noise control values are received from the environment module.

In a next step 330, the objects are grouped based on speed, size and relative distance between the objects. Accordingly, one or more traffic groups are formed. A traffic group is a group of stationary or dynamic or combination of both objects on road that move at relatively similar speed. A traffic group may include one or more vehicles on the road. In a next step 335, ‘Average Transition Speed’ i.e., (ATS) of each of the traffic group of objects of a predefined portion along said road are identified. For example, along the road with a range of 2 km a predefined portion three different traffic groups are identified that move at same or different speeds. The average transition speed is obtained by using the stationary parameters and the dynamic parameters.

In one embodiment the secondary control unit 106 includes a dynamic module that is configured to identify the relative motion between any two objects captured in the images. The dynamic module obtains the relative distance between any two or more objects over a period, for example, 30 seconds, 60 seconds etc. Accordingly, the dynamic module derives the traffic dynamics, for example, the speed of vehicles, relative distance between two vehicles, rate of movement of vehicles relative to any object and the like. In another embodiment, the secondary control unit 106 includes an environment module that is configured to receive the sound/noise levels and air pollution levels generated in a predefined territory of the traffic management system 100. The system 100 additionally includes one or more sound sensors that record the sound/noise around the system 100. The sound recorded by the sensors is received by the primary control unit 104.

Accordingly, the environment module of the primary control unit 104, processes the inputs of the sound/noise sensors and air pollution sensors to determine the sound and air quality level. The environment module compares the noise and air quality values received from the respective sensors with standard sound values in the databases. As a result, the assessment of the sound level that complements traffic density is figured out by the system 100.

The secondary control unit 108 includes a processing module 244 that is configured for receiving the data from the image processing module 232. The processing module 244 includes a first module 248, a second module 252, a third module 256 and a fourth module 260. In accordance with the present invention, the first module 248 is configured to identify, predict the traffic congestion, and resolve the congestion. The first module 248 analyzes the image data received from the image processing module 232. Further, the first module 248 processes the image data and identifies the traffic congestion using artificial intelligence and object detection algorithms. In accordance with the present invention, the first module 248 is trained by the secondary control unit 106 based on predefined dataset to selectively activate the traffic signals 108 for predefined amount of time for resolving the traffic congestion in a particular situation. For example, the traffic congestion on a four-way road crossing is identified by the first module 248 and resolved by selectively activating and deactivating the signals for predefined amount of time.

The second module 252 is configured for identifying a predefined object, for example, special vehicles or emergency vehicles such as ambulances, fire brigade or the like passing through road. The second module 252 identifies the emergency vehicles through the image data received from the image processing module 232. The identification of the object is done by identification of shape, colours, sounds, speed of the respective objects. Further, the second module 252 communicates with the first module248 for receiving the analyzed values/details of the traffic congestion of a particular road.

Further, the second module 252 identifies the special vehicles arriving on a particular location or road and activates the secondary control unit 106 for changing the traffic lights. For example, if the emergency vehicle is approaching at traffic congested road and the traffic signal is red than the second module 252 communicates with primary control unit 104 for changing the signal to allow the emergency vehicle to pass the road.

The third module 256 is configured for identifying natural calamities, road closure or the like. The third module 256 communicates with the first module for analyzing the traffic congestion in a particular area. This is done by receiving signals from the sensors such as the sound sensor, temperature sensors, pollution sensors, motion sensors in addition to the analysis of objects performed by the second module and the image processing module. In accordance with the present invention, the third module 256 is configured for identifying and detecting natural calamities for example flood situations, road blockage due to fallen trees in the particular area or the like.

The third module 256 identifies the road closure situations, natural calamities by comparing the image data received from the image processing module with the predefined dataset. The third module also selectively considers inputs from the various sensors. The third module 256 after detecting the natural calamities or road closure situations notifies the secondary control unit 106. The secondary control unit 106 further activates the traffic signal 108 to illuminate as per the instructions of the third module.

The reference data module 240 performs the real time analysis of data from primary control unit 104 and optimizes the working of the traffic light system 108. The ML module 236 obtains data from reference data or from the real time data received from the primary control unit 104.

In accordance with the present invention, the system 100 is installed and integrated with one or several traffic signal 108 along one or more roads. In such a situation, the primary control units 104, 104’, 104”, 104”’ are installed on every traffic signal 108 and all of the primary control units 104, 104’, 104”, 104”’ communicate with the secondary control unit 106. Accordingly, the traffic signals 108 along the road are controlled and synchronized with another traffic signal 108 for clearing the traffic along said road.

In operation, referring to FIGS. 1 and 2, the system of the present invention 100 is integrated with existing traffic light systems 108 at various locations on roads network 116. In an initial step, the sensors 124 capture real time images and send it to the controller 128. Further, the controller 128 processes the received data and sends it to the communication module 136. The GPS module identifies the location of the primary control unit 104. Further, the data transfer module transfers the data between the primary control unit 104 and the secondary control unit 106. The internet module connects the primary control unit 104 with the secondary control unit 106. Further, the communication module 136 receives processed data from the controller 128 and sends the data to the secondary control unit 106.

The IOT hub module 200 receives the data from the primary control unit 104. The IOT hub module 200 forwards the data to the event hub module 204. The event hub module streams the data on a cloud or remote server. The traffic data consumer module 224 is configured to analyze the data in the blob storage 220 that is received by the machine learning module 236. Further, the stream analysis module 212 is responsible for analyzing the data received from the primary control unit 104. Further, the image processing is performed 232 on the analyzed data and the output is received by the processing module 244. The secondary control unit 106 routes the control to either the first module 248, the second module 252 or the third module 256 based on the requirement of the user.

The dynamic module is configured to identify the relative motion between any two objects captured in the images. Further, the audio module is configured to receive the sounds generated in a predefined territory of the traffic management system 100. The processed data is received back to the secondary control unit 106. The secondary control unit 106 further communicates the data to the primary control unit 104.

The controller 128 of the primary control unit 104 accordingly, illuminates the traffic signal lights 108 as per the data processed by the secondary control unit 106. The secondary control unit 106 analyzes and processes the received data for resolving traffic congestion, recognizing emergency vehicles and natural calamities related issues. The primary control unit 104 optimizes the illumination of the traffic lights 108 in accordance with the instructions received from secondary control unit 106.

In another situation an emergency vehicle which is travelling through the road is identified by the traffic management system 100 and accordingly predefined signals are activated. In a situation of natural calamity for example in case of water flooding the water sensors and the sound sensors input are considered by the second module and accordingly the signals are activated.

The system 100 enables management of traffic to avoid the traffic congestion for traffic flow in one direction during peak hours. The system 100 collects the data of incidences like vehicle breakdown, water logging, accident, tree collapse, etc. in near real time and notifies to the nearest traffic control room for immediate and appropriate action. Based on notification from medical institutes / hospitals, the system 100 enables preparing green corridor for ambulance/medical emergency vehicle in near real time.

Based on notification from city police, the system 100 enables identifying wanted criminal or missing person or wanted vehicle in near real time. The system 100 can provide reports with cleanliness score for nearby areas that can further be utilized by city authorities to take corrective actions. The system 100 enables improved traffic management, addressing the day-to-day hassles of public and traffic police.

The foregoing description of specific embodiments of the present invention has been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching.