Big Traffic Data Analytics For Smart Urban Intelligent Traffic System Using Machine Learning Techniques

Hlaing, Su Su; Tin, Mie Mie; Khin, Mie Mie; Wai, Phyo Phyo; Sinha, G. R.

doi:10.1109/gcce50665.2020.9291790

Cited by 13 publications

(8 citation statements)

References 4 publications

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“…Nowadays, vehicles are equipped with a large number of perception devices, such as accelerators, radars, cameras, and advanced data processing units. With these abilities, vehicles can provide multiple location-based services, such as realtime map building [1], traffic management [2], crowdsensing [3], and environmental monitoring [4,5], etc,. With the development of intelligent transportation system (ITS), a large number of perceptual data is continuously generated [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

Wu¹,

Wang²,

Zhang³

et al. 2023

Preprint

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As the fog-based internet of vehicles (IoV) is equipped with rich perception, computing, communication and storage resources, it provides a new solution for the bulk data processing. However, the impact caused by the mobility of vehicles brings a challenge to the content scheduling and resource allocation of content dissemination service. In this paper, we propose a time-varying resource relationship graph to model the intertwined impact of the perception, computation, communication and storage resources across multiple snapshots on the content dissemination process of IoV. Based on this graph model, the content dissemination process is modeled as a mathematical optimization problem, where the quality of service of both delay tolerant and delay sensitive services are considered. Owing to its NP-completeness, the optimization problem is decomposed into a joint link and subchannel scheduling subproblem and as well a joint power and flow control subproblem. Then, a cascaded low complexity scheduling algorithm is proposed for the joint link and subchannel scheduling subproblem. Moreover, a robust resource management algorithm is developed for the power and flow control subproblem, where the channel uncertainties in future snapshots are fully considered in the algorithm. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

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Section: Introductionmentioning

confidence: 99%

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

Wu¹,

Wang²,

Zhang³

et al. 2023

Preprint

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show abstract

“…Light-duty vehicles, such as passenger cars and pick-up trucks, are used for personal transportation and light-duty work and deliveries. Each type of vehicle is designed to meet the specific needs and requirements of its intended use, and all are critical components of the transportation and logistics system [5]. As the number of vehicles engaged in traffic accidents increases, the danger of injury and death continues to rise.…”

Section: Introductionmentioning

confidence: 99%

Yus - A Deep Learning Algorithm for Collision Avoidance through Object and Vehicle Detection

Beg¹,

Ismail²,

Miah³

et al. 2023

ARASET

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One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective was to create a system for detecting vehicles, motorcycles, and traffic signals on the roads in University Malaysia Pahang using object detection techniques. The video was taken through Go-Pro camera to capture video footage of traffic objects on the roads in the district, which was then analysed using the YOLO-V8 deep learning algorithm. The system was trained on a pre-existing dataset of 1,068 images, with 70% of the dataset used for training and 30% for testing. After conducting a performance validation, the system achieved a mean average precision of 88.2% on training dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. The results of this study could be beneficial for road safety authorities and researchers interested in developing intelligent transportation systems.

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“…With the development and progress of science and technology, more and more Internet of Things devices are connected to our life. It has provided us with more and more help in intelligent grid [1], intelligent transportation [2], smart home [3], and public safety [4], making life more convenient. Internet of Things devices collect all kinds of sensor information through various information sensors, radiofrequency identification technology, global positioning system, infrared sensors, laser scanners, and other devices and technologies and then connect all kinds of objects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

Xie

Luo

et al. 2022

Wireless Communications and Mobile Computing

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More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.

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Big Traffic Data Analytics For Smart Urban Intelligent Traffic System Using Machine Learning Techniques

Cited by 13 publications

References 4 publications

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

Yus - A Deep Learning Algorithm for Collision Avoidance through Object and Vehicle Detection

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

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