Machine Learning Approach on Traffic Congestion Monitoring System in Internet of Vehicles

Kamble, Shridevi Jeevan; Kounte, Manjunath R

doi:10.1016/j.procs.2020.04.241

Cited by 60 publications

(22 citation statements)

References 19 publications

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“…This paper shows that approaches to machine learning (ML) can be useful for prediction of current and historical data in real-time traffic and future traffic and short-term traffic. There were three separate time slots used for tracking traffic congestion that contributed to the assessment of vehicle average speed [18].…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Khedkar

Ramalingam

Najafi

2021

Wireless Communications and Mobile Computing

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An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

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Section: Related Work and Research Gapmentioning

confidence: 99%

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Khedkar

Ramalingam

Najafi

2021

Wireless Communications and Mobile Computing

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“…More-over, ML and AI-based SPS can predict parking lot occupancy status of the upcoming days, weeks, or even months and provide a dynamic pricing scheme. ML-based systems can monitor traffic congestion of particular roads and offer a smart solution to smart parking spaces [19].…”

Section: Artificial Intelligence Based Spsmentioning

confidence: 99%

A Novel Intelligent Image-Processing Parking Systems

Naraharisetti¹,

Greenfield²,

Placzek³

et al. 2022

Artificial Intelligence and Machine Learning

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The scientific community is looking for efficient solutions to improve the quality of life in large cities because of traffic congestion, driving experience, air pollution, and energy consumption. This surge exceeds the capacity of existing transit infrastructure and parking facilities. Intelligent Parking Systems (SPS) that can accommodate short-term parking demand are a must-have for smart city development. SPS are designed to count the number of parked automobiles and identify available parking spaces. In this paper, we present a novel SPS based on real-time computer vision techniques. The proposed system provides features including: vacant parking space recognition, inappropriate parking detection, forecast of available parking spaces, and directed indicators toward various sorts of parking spaces (vacant, occupied, reserved and handicapped). Our system leverages existing video surveillance systems to capture, process image sequences, train computer models to understand and interpret the visual world, and provide guidance and information to the drivers.

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“…Moreover, ML and AI-based SPS can predict parking lot occupancy status of the upcoming days, weeks, or even months and provide a dynamic pricing scheme. ML-based systems can monitor traffic congestion of particular roads and offer a smart solution to smart parking spaces [ 89 ].…”

Section: Approaches To Smart Parking Systemmentioning

confidence: 99%

Smart parking systems: comprehensive review based on various aspects

Fahim

Hasan

Chowdhury

2021

Heliyon

122

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Parking allocation has become a major problem in modern cities for which numerous smart parking systems (SPS) have been developed. This paper aims to provide comprehensive study, comparison and extensive analysis of SPSs in terms of technological approach, sensors utilized, networking technologies, user interface, computational approaches, and service provided. Moreover, the paper fills up the research gap by providing a clear insight into the suitability of SPSs in various environmental conditions and highlights their advantages/disadvantages. The extensive comparison among multiple aspects of SPSs would enable researchers, designers, and policymakers to identify the best suited SPS and understand the current trends in this sector.

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Machine Learning Approach on Traffic Congestion Monitoring System in Internet of Vehicles

Cited by 60 publications

References 19 publications

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

A Novel Intelligent Image-Processing Parking Systems

Smart parking systems: comprehensive review based on various aspects

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