IOT- AND MACHINE-LEARNING-DRIVEN TRAFFIC MANAGEMENT SYSTEMS FOR NEXT-GENERATION SMART CITIES

Abstract

IOT- AND MACHINE-LEARNING-DRIVEN TRAFFIC MANAGEMENT SYSTEMS FOR NEXT-GENERATION SMART CITIES The method for the development of a predictive analytics model is the foundation of a machine learning framework for controlling air pollution and traffic infrastructure in urban areas. Based on data collected from multiple sources throughout the city, the model uses transportation data to forecast traffic patterns. These factors are used to find seasonal patterns in the data that are based on time series. Machine learning and the Internet of Things are transforming data collection, analysis, and presentation in all sectors of the economy and have the potential to significantly enhance transportation systems. The symbiotic technologies billions of datapoints and state-of-the-art neural network techniques to shed light on how transport networks behave. To reduce traffic jams, offer safe data transfer, and identify collisions The traffic management system is based on the Internet of Things. Autonomous vehicles and smart devices use an IoT-based ITM system with a collection of sensors to detect, collect, and transmit data. Machine learning is being used to enhance the transportation system.

Specification

Description:IOT- AND MACHINE-LEARNING-DRIVEN TRAFFIC MANAGEMENT SYSTEMS FOR NEXT-GENERATION SMART CITIES

Technical Field
[0001] The embodiments herein generally relate to a method for IoT- and machine-learning-driven traffic management systems for next-generation smart cities.
Description of the Related Art
[0002] A low-cost transport sensor based on computer vision has been designed and produced by Vivacity Labs as a crucial step towards more intelligent transport systems. This paper details the installation of these sensors throughout the city and the insights that were subsequently produced by the Milton Keynes-based Viva MK project, which was funded by Innovate UK. The results demonstrate that the deployment of intelligent transportation systems improves air quality and transportation. The study's findings are examined and linked to real-world uses in the fields of intelligent transportation and air pollution reduction. Speeding, reckless driving, fatigued drivers, stray animals on the road, and poor infrastructure are the main causes of traffic accidents. The majority of fatalities and injuries in these incidents are caused by the emergency medical services' delayed response. The period immediately following a traumatic injury is referred to as the "golden hour," during which rendering immediate, lifesaving surgery and medical care raise the likelihood of human survival by a mean of one-third. These modern technologies, as outlined by Blue Orange Digital, a top-ranked AI consulting and development agency in NYC, enable applications ranging from waste management to food supply optimization and healthcare digitization. In the process, they are disrupting entire industries and creating new business opportunities and applications.
[0003] Autonomous vehicles, vehicle-to-infrastructure systems, and intelligent traffic signal control are just a few of the notable developments occurring in the field of intelligent transport systems (ITS). However, manual traffic surveys and induction loop sensors continue to be a major component of the majority of transport planning and management systems. These methods only offer a glimpse into how a city's road network behaves, and they are rather costly. New traffic sensor technologies are therefore starting to appear with the goal of offering more spatial granularity and richer information at a price that permits the deployment of more sensors. Numerous objects, such as intelligent roadways, cooperative transportation systems, and automated cars, are now directly connected to the Internet of Things (IoT) for Information Technology Management (ITM). This enhances data transmission and produces a variety of connectivity and low-bandwidth devices in high-capacity locations worldwide. NSO research predicts that India, an emerging country, will see a 7.7 percent decline in GDP. City traffic is a highly dynamic environment, where thousands of participants using different transportation modalities interact in complex manners. Furthermore, in order to guarantee the security and welfare of every traffic participant, decisions must be made instantly.
[0004] If a lot of image data with high-quality annotations is available, these models have comparable potential for traffic management tasks. Based on this precedent, Vivacity Labs has created and produced a traffic sensor that processes video analytics at the edge and sends the anonymous data it has extracted to a cloud-based system for display and additional analysis. Although using these sensors to collect vehicle speed and congestion metrics has been demonstrated, even more sophisticated features are likely to follow. However, for the purposes of this paper, only data pertaining to the count and classification of road vehicles was used. An ITM system is a commonly used technique to address traffic management issues.
SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for IoT- and machine-learning-driven traffic management systems for next-generation smart cities. In some embodiments, wherein the deployed sensor network produces occupancy data for 93% of the city's paid parking spaces, which adds up to 104 junctions, 812 carriageways, and 11388 parking spaces. It also produces classified count data at the entrances and exits of every major junction in the 100km2 Milton Keynes area. The driving experience and level of concentration are determined using the cloud picture API. The images of the traffic intersection it follows are captured and stored in the cloud database. The matter is also moved to the following traffic light. The currently operational traffic signal will keep an eye on the performance of the subsequent traffic light and then proceed according to the situation. For city planners in particular, data is power; it is now required that their decisions be supported by data. Mobility data, or information about how people move around the city, can give important clues about what people need for transportation. It gives them a precise picture of how various city pathways are being used, which raises the likelihood of more precise, citizen-friendly planning.
[0002] In some embodiments, wherein the system was created using cybersecurity best practices and to guarantee the protection of individual privacy. In order to comply with GDPR, video streams are processed and then discarded on-sensor, ensuring that no personally identifiable information is removed from the devices. Only aggregated, non-identifiable traffic data is sent to a cloud-based system for analytics and storage after leaving the device. Stakeholders can then safely access the data and insights that are subsequently extracted through a graphical user interface and standardized APIs. Vehicle congestion assessments were widely used in the road traffic, utilization, and average densities. Most of this information was obtained from pictures and videos that machine vision software had taken. An IoT-enabled monitoring system was presented by Sequeira et al.'s authors for this particular scenario in order to gather, process, and compile actual traffic patterns. This data is being collected by a number of applications, which use it to power services aimed at consumers. Analytics frameworks, on the other hand, make it simple to draw conclusions from these diverse data sources. It is feasible to use this rich mobility data to enhance the planning process by sharing it with the city administration and planners.
[0003] In some embodiments, wherein after that, the sensors analyze the data and extract aggregated metrics in real-time, like average speed per unit time or counts per unit time. These metrics are then sent to the Vivacity Cloud for analytics and aggregation at the city scale. There is a pending patent for this entire data collection methodology. The technique illustrates traffic crashes using graphic elements that are temporally ordered. To sum up, the system design includes a phase for identifying transient patterns and extracting visual features. Convolution and recurrent layers are used to learn sensory and spatial features during the learning phase. The advantages of crowdsourced mobility data can result in shorter commutes and better walkability. Car drivers will spend less time in city centers waiting for pedestrians and traffic lights, while bike riders will benefit from more efficient routes and greener pathways. Every traffic participant benefits from mobility data.
[0004] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for IoT- and machine-learning-driven traffic management systems for next-generation smart cities according to an embodiment herein; and
[0003] FIG. 2 illustrates a method for the layered architecture of IoT according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 illustrates a method for IoT- and machine-learning-driven traffic management systems for next-generation smart cities according to an embodiment herein. In some embodiments, the pedestrians, cyclists, motorcyclists, cars, vans, OGV1 and OGV2, and buses are the eight main groups of road users that the sensors can count and categorize. The sensors' software can be updated and communicated with remotely because they are powered by street lighting and use 3G for communication. The accident alert sound system tracks the accident after the application layer first keeps track of the vehicle's location and image tracking. The subsequent layer, known as the service layer, collects data and pre-processes it. The network layer, the third layer, is where data communication takes place. Vehicle data is securely transferred in this layer using Secure Early Traffic-Related EveNt Detection. Both cars and roadside cameras can be equipped with computer vision and video analytics. Algorithms are capable of doing calculations on the fly and are able to identify behavioral and situational anomalies as soon as they occur. Computer vision enables a wide range of applications, from automatically reading license plates to identifying walking patterns. They can reduce the significant risks connected to irresponsible driving and guarantee the security of public pedestrian zones when incorporated into traffic management systems. In order to improve the overall efficiency of the transportation system, the paper incorporates machine learning (ML) techniques to supplement the IoT infrastructure. A comprehensive approach to traffic management is made possible by the combination of IoT and ML technologies, which use data-driven insights to make better decisions.
[0003] In some embodiments, a decision-makers can significantly reduce disruption before symptoms develop and interact if they are aware of impending problems. Thus, the research and development of a city-scale prediction engine was part of the project. To assess the models' advantages and disadvantages for practical implementation, a comprehensive literature review and comparative analysis of 21 promising short-term traffic prediction model types were carried out. Information is gathered in the initial stage utilizing the sensors and photography equipment. An important component of ITM is preparing data after sensors or cameras have collected it. Techniques for estimating missing values are used when preprocessing data. The processing method is used to process the collected data, and the training method is used to train the dataset. The vehicle's exact location and traffic data are collected. All types of vehicles can now communicate direction, speed, and travel times thanks to sensor technologies and sophisticated wireless communication protocols. Because IoT devices are more customizable, there is no limit to the amount of information they can exchange. They enable the gathering and sharing of contextual information from the surroundings in addition to being able to be fastened to any moving object. The symbiotic relationship between these technologies is especially significant because machine learning algorithms are able to forecast traffic density and flow with remarkable predictive power thanks to the strength of large datasets that incorporate a variety of factors, including weather, traffic patterns, and real-time vehicle movements.
[0004] In some embodiments, the most promising method for serving as the foundation for additional research was probably the term memory (LSTM) neural networks. A system was created that cleans and combines all of the real-time sensor data before feeding it into a neural network that forecasts the overall vehicle flows for each of the input data streams over a 24-hour period. Features found at a time interval (TI ii) for the frame (FR ii) are chosen and tracked for a threshold number of frames if the estimated total individual movement is large enough. Nearly all newly extracted features are connected to the currently recorded characteristics by a Euclidean distance minimum. Active traffic management strategies can leverage the advantages of sensor-based solutions. In addition to facilitating short-term prediction and control, they may result in less traffic and more fluid traffic. In any contemporary transportation management system, IoT-based sensor technologies are essential for assisting traffic management organizations in reducing emissions, noise, and travel times. IoT devices serve as essential data gathering tools, guaranteeing a constant flow of traffic data in real time. The creation of dynamic traffic systems that can adapt to changing circumstances is made easier by this constant flow of data. The creation of more effective and responsive traffic management systems that are suited to the complex dynamics of urban environments has advanced significantly with the combined use of machine learning and IoT.
[0005] FIG. 2 illustrates a method for the layered architecture of IoT according to an embodiment herein. In some embodiments, a machine learning model that employs a sophisticated configuration of LSTM cells to balance learning regular daily patterns with reacting to fleeting changes in the road network generates the predictions. Deep learning models provide the best performance at longer prediction horizons, according to the results of a comparative analysis, and this model type was selected based on strong evidence from recent literature. An intelligent traffic management (ITM) system can reduce the number of unintentional fatalities (MVCs) and the probability of accidents by implementing intelligent traffic control during MVCs. A system that operates outside the vehicle is necessary for accident detection and alarm generation for oncoming vehicles in order to prevent MVCs. An innovative concept for building intelligent highways is offered here. Smart roads (SRs) are equipped with a variety of sensors and actuators to automatically detect collisions. In SRs (a different system), nodes are separated by about 50 meters. Since these nodes are connected to the road's side, you can utilize their previously saved locations. The quality of management applications also improves when data is utilized for decision-making and to better understand the dynamics of city travel. This guarantees that future infrastructure development projects and traffic control strategies will precisely match the needs of the populace. We are excitedly awaiting the day when AI and IoT become the new technological standard. More accurate route selection is made possible by the proposed ATM-ALTREND system. The model's quality is assessed using the test's lower limit precision level. Let's assume, though, that the proposed model yields the lower bound with the proper degree of accuracy. If this is the case, it is clear that there are effective channels of communication and that all other, less successful ones have been closed.
[0006] In some embodiments, the model learns and applies information about how traffic in the city impacts future traffic at each specific location because the prediction is multivariate in both the input and output dimensions. Additionally, the data is "lagged" backwards in time, which means that the model receives data from multiple prior timesteps at each timestep. Another alternative way to alert drivers is to use a loud siren, which they can hear and use to stop an MVC, especially in BWC. These alerts are generated automatically. The suggested system will be put into place on both sides of the street. Nowadays, a sizable portion of the global population resides in urban areas. Approximately seven devices per user are anticipated to actively communicate over the internet in the future. Approximately 70% of the world's population is predicted to live in cities by the end of 2050. Therefore, the strategic development of urban areas will be the main challenge in the coming decades. Urban environments must be managed by transforming them in accordance with the principles of a smart city in order to satisfy the growing demand for services of higher quality. In SRs (a different system), nodes are separated by about 50 meters. Since these nodes are connected to the road's side, you can utilize their previously saved locations. A node that detects an accident alerts an EOC (Emergency Operation Centre) of its previously saved position in order to expedite the rescue effort and reduce damage. The key element of the SR is an AALS alert system, which alerts drivers of approaching vehicles or collisions.
[0007] In some embodiments, it was possible to quantify the likely error that can be introduced when using single-day counts to represent the mean at a location by analyzing the city-wide dataset generated by the deployed sensors network over an 8-month period. In order to determine the variation between Tuesdays which are typically regarded as representative days of the week 431 count sites were examined over 27 Tuesdays. Every public holiday and times when there were a lot of variation, like Christmas week, were eliminated from the data. A smoke sensor is used to detect fires. When a car crashes or an object is struck, the microphones record the sound and send it to the microcontroller, which compares the levels to a preset threshold. An accident is reported if the level rises above the cutoff. As a result, if the sound is not higher than the threshold, the microcontroller rejects it. These days, IoT-based infrastructure deployments are changing because of their significant contribution to the fields of academia and smart city manufacturing. The population of metropolitan areas is steadily growing, which makes civic life more difficult. According to a United Nations Population Fund forecast, 50% of the population lived in urban areas in 2007, a situation that is predicted to significantly change by 2030. It is expected that IoT-based infrastructure will be crucial to many applications for intelligent companies and city dwellers. There is an audible sound made when glass breaks; a burning car releases smog and raises the temperature of the atmosphere; and a car stopping in the middle of the road poses a risk to other cars. By taking into account each of these factors, an ideal accident detection system can be created.
, Claims:1. A method for IoT- and machine-learning-driven traffic management systems for next-generation smart cities, wherein the method comprises;
including cameras, GPS trackers, and environmental sensors, are continuously collecting data on vehicle movements, traffic density, weather conditions, and road incidents;
analyzing collected data to understand traffic patterns, detect congestion trends, and predict high-traffic areas or times;
optimizing traffic signal timings, adjusting lane assignments, and suggesting alternate routes;
integrating data from public transit systems, the platform is helping to synchronize buses, trains, and other transportation modes with traffic signals, ensuring smoother commutes and encouraging the use of public transport;
monitoring emissions, noise levels, and air quality to assess the impact of traffic on urban pollution;
Using historical and real-time data, the machine learning models are predicting future traffic scenarios, which can help in planning for high-demand periods, like rush hours or public events, improving overall city mobility;
speeding up the response time, it minimizes the risk of further congestion or accidents;
sharing data with navigation apps, the system is keeping drivers informed with real-time route recommendations, delays, and road closures, enabling more efficient and informed travel decisions;
evaluating its performance by analyzing metrics like average travel times, traffic density reductions, and user satisfaction; and
designing a modular IoT infrastructure, the system is enabling scalability, allowing cities to add more sensors, incorporate new data sources, or upgrade algorithms as population and traffic demands grow.

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