2022· International Journal of Applied Data Science & Modern Computing· Vol 5, pp. 01-14· 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 Intelligent Traffic Management Systems (ITMS) have become an important feature of the smart city infrastructure because of the fast increase in the city population and the consequent urban traffic congestion, fuel use, and road accidents. Conventional methods of traffic management apply a lot on the operation of fixed-time control mechanisms and rule-based systems, which are not flexible to the dynamic traffic conditions. In the recent past, progress in the field of deep learning has resulted in the creation of data-driven systems of traffic management that can learn intricate spatial and temporal patterns of traffic through major sources of heterogeneous data. The paper provides a detailed research on designing, implementing and testing of an Intelligent Traffic Management System based on deep learning. The suggested system combines convolutional neural networks (CNNs) to estimate the traffic density, recurrent neural networks (RNNs) and long short-term memory (LSTMs) to predict the traffic flow, and reinforcement learning (RLs) to control traffic signals. Various data sources such as live video streams, sensor data and past traffic data are used to improve accuracy of predictions and effectiveness of decisions. The proposed system architecture includes a modular architecture that will include all the layers of data acquisition, preprocessing, model training, and real-time deployment. Numerous experiments on benchmark traffic datasets have shown that congestion is greatly reduced, the average vehicle waiting time is minimized and the traffic throughput is much improved in comparison to traditional systems. 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. This paper has given relevant information about the application of AI-driven traffic control systems in practice and opened up the prospects of future research in intelligent transportation systems.
Ibrahim A. Lawal· International Journal of Art...· 0 citations
As urbanization continues to reshape large cities, traffic congestion remains a persistent challenge for urban transportation systems. Using Beijing as a case study, this paper examines the spatiotemporal evolution of urban traffic congestion from a deep learning perspective based on multi-source data. The results suggest that traffic congestion in Beijing displays a pronounced “dual-peak” pattern associated with daily commuting activities. Compared with the pre-pandemic period, weekday travel demand has generally recovered and in some cases exceeded previous levels, whereas holiday travel remains relatively subdued, accompanied by increasingly concentrated travel behavior. Among the models considered, Long Short-Term Memory (LSTM) performs particularly well in capturing nonlinear variations and temporal dependencies in traffic flow, leading to improved prediction accuracy. Between 2020 and 2025, the congestion index experienced a trajectory of decline, recovery, and subsequent stabilization, a pattern that appears to be associated with the gradual implementation of intelligent traffic management measures. These findings contribute to a better understanding of recent changes in urban traffic dynamics and may offer useful insights for future traffic planning and governance.
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. Balu, Narasimha Rao, S. Lakshmi et al.· 2026 7th International Confe...· 0 citations
High traffic volume, urbanization and car ownership have exacerbated traffic congestion, travel time and road accidents; these are some of the problems facing modern transportation systems. Artificial Intelligence (AI) has become a viable solution, allowing intelligent, adaptive and data-informed traffic management. This paper provides 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. It discusses their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles. The research also covers the features of Vehicle-to-Everything (V2X) communication, connected vehicles, the Internet of Things (IoT), and Intelligent Transportation Systems (ITS), as well as their potential for enhancing transportation efficiency and road safety. In addition, the paper points out potential roadblocks for the implementation of AI such as data quality, computational complexity, cybersecurity, privacy, infrastructure cost, and model interpretability. Finally, future research directions are outlined, highlighting explainable AI, generative AI, digital twins and integration of intelligent transportation systems in smart cities for sustainable cities. Overall, the review shows that AI can revolutionize traditional transportation systems, turning them into intelligent networks that can help alleviate congestion, lower the risk of accidents, optimize traffic flow, and facilitate safer and more sustainable urban mobility.
Shaikh Amra Bano, Kamal, Pramod Kumar Soni et al.· International journal of com...· 0 citations
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