Naïve Bayes Classifier Based Traffic Prediction System on Cloud Infrastructure

Aung, Swe Swe; Naing, Thinn Thu

doi:10.1109/isms.2015.45

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…However, with most cars having a GPS device and the commonality of cellphones with every driver, many approaches use the data from the GPS along with weather and generic traffic flow information to determine traffic prediction. The nature of traffic flow prediction using sensing modalities such as GPS require systems to be operated as cloud-based systems as is the case in [213][214][215][216]. Of these, Wangyang et al [215] and Xiao et al [216] use deep learning based sequential modeling approaches to predict traffic flow ahead of time where as Aung & Naing [213] and Yunxiang Liu & Wu [214] solve this through a classification formulation.…”

Section: Smart Transportmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Transportmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…The authors of [29] used the data acquired from the GPS receiver and the history data to detect traffic conditions by analyzing the behaviour of the vehicle primarily. The tests were performed on the road between Baham Campus to Hlaing Campus and Insein road by one car.…”

Section: Studies Performed In Africamentioning

confidence: 99%

“…Aung and Naing [29] The system predicts the current traffic status by watching the available vehicles with few GPS receivers; The system implements the Bayes classifier to obtain better results; The system identifies if the phone is in a vehicle or walking.…”

Section: Studies Pros Consmentioning

confidence: 99%

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Identification of Warning Situations in Road Using Cloud Computing Technologies and Sensors Available in Mobile Devices: A Systematic Review

Pires

García

2020

Electronics

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The use of mobile devices connected continuously to the cloud is increasing, and the development of a cloud-based solution may power the function of these devices in mobility. Several types of sensors available in the mobile devices may allow the acquisition of different kinds of data, including inertial sensors, magnetic sensors, location sensors, acoustic sensors, and imaging sensors. The primary purpose of this study is to review the methods, features, and studies related to the identification of road conditions and warning situations. We performed systematic research to discover relevant studies written in English for the identification of different situations using the sensors available in the mobile devices, published between 2011 and 2019. After that, we analyzed the remaining studies to verify its reproducibility. The major part of the studies does not report the accuracy in the detection of warning situations. As future work, we intend to develop a system based on the Centre of Portugal for the detection of warning situations, road problems, and other issues verified during driving activities. As future work, we intend to develop a system using only a mobile device for the acquisition of sensors data in the centre of Portugal. We verified that the majority of the studies were performed in big lands, but in small areas, the number of accidents and road abnormalities is also high.

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“…Time series [3], and neural network models [4] [5], are often applied to prediction traffic flow and predict traffic congestion based on vehicle speeds, weather, incident, and special days of historical data. Some studies use a Bayes classifier to predict traffic congestion [6] [7]. Other used non-parametric regression k-NN to predict short-term traffic flow [8][9][10] [11].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Priambodo

Jumaryadi

2018

MATEC Web Conf.

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During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

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Naïve Bayes Classifier Based Traffic Prediction System on Cloud Infrastructure

Cited by 12 publications

References 3 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Identification of Warning Situations in Road Using Cloud Computing Technologies and Sensors Available in Mobile Devices: A Systematic Review

Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

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