2022· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
The paper is a detailed research of how big data analytics have been used to predict traffic flow and analyses sources of data, analytics, machine learning and deep- learning models and scalable processing frameworks in modern traffic prediction systems.
Abstract
Traffic jam is currently one of the most critical issues in contemporary cities because of the high rates of population growth, the possession of vehicles, and the insufficient development of the road system. Smart transportation systems (ITS) heavily rely on predicting traffic flow in the future to allow for proactive traffic control, reduce traffic congestion, optimize routes, and ensure increased safety of commuters. With the development of big data analytics, the prediction of traffic flows has been changed greatly since it taps into large amounts of heterogeneous data produced by sensors, GPS, mobile phones, social media, and intelligent vehicles. The paper is a detailed research of how big data analytics have been used to predict traffic flow. It analyses sources of data, analytics, machine learning and deep- learning models and scalable processing frameworks in modern traffic prediction systems. The most popular traditional statistical models, state-of-the-art deep learning methods convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and graph neural networks (GNN) were mentioned through an extensive literature survey. The suggested approach incorporates data preparation, feature detection, model training and performance analysis in a big data ecosystem. It has been experimentally shown that advanced analytics can be effectively implemented to enhance the accuracy of predictions and make them robust. The paper is summarized by a discussion on challenges, limitations as well as future research directions in the prediction of traffic flow using big data.
The findings confirm that traffic management systems based on deep learning can contribute significantly to the improvement of urban mobility, environmental impact, and road safety.
Ibrahim A. Lawal· International Journal of Art...· 0 citations
Examination of the spatiotemporal evolution of urban traffic congestion in Beijing from a deep learning perspective based on multi-source data suggests that traffic congestion in Beijing displays a pronounced “dual-peak” pattern associated with daily commuting activities.
Zihan Zhou· Computers and artificial int...· 0 citations
Smart city transport networks must be highly adaptive, meaning they can quickly adjust to new road conditions. This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing. In order to correctly analyse traffic and detect accidents, the platform continuously gathers heterogeneous data from roadside cameras and automobile sensors, performs essential analytics at the edge to decrease latency, and runs robust cloud analytics. The software is able to do precise traffic analyses and detect accidents because of this. Abnormal traffic event spatial and temporal patterns are captured using a mixed deep learning architecture employing recurrent neural networks and convolutional neural networks. Also, for proactive traffic management, a module that forecasts traffic patterns can be used. The proposed system exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy, according to the experimental results. The system is both scalable and inexpensive, and it improves urban mobility, response times to emergencies, and road safety.
R. Elankavi, Imran Alam, Mogadala Mounika et al.· ITM Web of Conferences· 0 citations
High growth of urban populations poses numerous challenges to the urban transport system that are characterized by long travel time, congestion, and pollution. Traditional techniques of traffic management may not be responsive enough to these problems because it is seldom able to react promptly to changing, real-time traffic scenarios. The study explores the computational traffic flow, mobility analytics, machine learning, and other subfields of informatics, like reinforcement learning and optimization methods, to analyze traffic management and the improvement of urban mobility analytics. The new approach is proposed, which forecasts traffic, traffic jams, and real-time management of traffic lights by synthesizing real-time data of traffic sensors, GPS, and city cameras. Deep Neural Networks are a form of machine learning that predicts traffic demand. Traffic signal timing control is performed using reinforcement learning. Genetic algorithms and particle swarm optimization are some of the optimization methods used to offer real-time route suggestions to minimize congestion and offer better travel times. The performance of the system is compared against the traditional methods, and the optimization of the traffic flow and the informatics analytics enhancement of the performance of the urban mobility system by the new method outperforms traditional methods in overwhelming ratios. The new regime reduces the waiting times by 1/4 and boosts the vehicular traffic flow by 1/3, and also reduces the fuel consumption of vehicles by 1/5, which reduces the CO2 emission by the city by 15%. The system was shown to be able to adjust to different conditions of traffic, such as peak and off-peak traffic. The system performance in the latter sections provided the research directions that were to be taken in the next stage, including incorporating autonomous vehicles and intelligent city models, and applying the advanced technologies of deep learning to enhance urban mobility and facilitate the creation of sustainable and efficient urban transportation.
Priya Vij, Ashu Nayak· 2026 International Conferenc...· 0 citations
Traffic monitoring in cities is highly significant in transportation planning, in determining the extent of traffic conditions, and the operation of smart cities. However, conventional approaches, such as counting manually and relying on sensors installed in the infrastructure, have some significant issues. Manual techniques are also labor-intensive and difficult to maintain with time. Sensor-based systems, although automated, are expensive to install and maintain over time and these systems lack scalability. To avoid these issues, this study proposes an automated vehicle traffic analysis system, which applies deep learning and video analytics. Input video clips in the system are processed using the YOLO object detection model on a continuous live stream. Video frames are processed sequentially one after the other and combining the identified vehicles over time, in order to determine the density of the traffic and peak traffic periods, low traffic activity periods. This methodology involves live streaming recording, segmenting video into smaller clips, identifying frames, matching them in temporal sequence and statistical analysis to view traffic patterns. The data is also presented in the form of graphs and summary statistics to better demonstrate how the traffic flow varies with the time windows. The framework is easy to scale and affordable alternative to the conventional techniques, yet its functionality can be influenced by factors, such as video quality and the surroundings. Nevertheless, the experimental results show that the proposed methodology is competent when it comes to identifying the pattern of traffic activity based on the long-duration surveillance videos and, thus, makes it possible to make data-driven decisions in the smart transportation systems.
G. Rao, Somarouthu Lakshmi Sharanya, Naralasetti Bala Sai· 2026 7th International Confe...· 0 citations
An overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning, and their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles are discussed.
Shaikh Amra Bano, Kamal, P. K. Soni et al.· International journal of com...· 0 citations
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