Handbook of Smart Cities 2018
DOI: 10.1007/978-3-319-97271-8_12
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Intelligent Transportation Systems Enabled ICT Framework for Electric Vehicle Charging in Smart City

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Cited by 22 publications
(11 citation statements)
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“…Innovative and sophisticated approaches/tools are also needed to process the Big Data generated from the stakeholders [186]. For future connected vehicles, Vehicle-to-Infrastructure (so-called V2I) communication is required to facilitate the EVs for accessing the Internet via innovative technologies in mobile communications, such as Bluetooth, WiFi, 4G, and even 5G networks [187].…”
Section: Information and Communication Technologymentioning
confidence: 99%
“…Innovative and sophisticated approaches/tools are also needed to process the Big Data generated from the stakeholders [186]. For future connected vehicles, Vehicle-to-Infrastructure (so-called V2I) communication is required to facilitate the EVs for accessing the Internet via innovative technologies in mobile communications, such as Bluetooth, WiFi, 4G, and even 5G networks [187].…”
Section: Information and Communication Technologymentioning
confidence: 99%
“…Techniques devised in the following literature [30][31][32][33] effectively addressed the problem of selecting the CS based on closest distance or minimal waiting time. The CS-selection model devised in the literature [30][31][32][33] adapted the pricing method to reduce the congestion and increase the profit by fixing a price based on the number of EVs getting charged at every point. 33 In [34], the authors devised a method for charging EVs, where the traffic conditions and geographic location were integrated.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The CS-selection model devised in the literature [30][31][32][33] adapted the pricing method to reduce the congestion and increase the profit by fixing a price based on the number of EVs getting charged at every point. 33 In [34], the authors devised a method for charging EVs, where the traffic conditions and geographic location were integrated. The optimal path for charging was devised in the literature 35 and combined the power grid, EVs, and transportation network to offer users navigation for charging.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In (13), the transition probability can be calculated as (14) where N ij is the number of transition times switching from one state to another. When taking advantage of MC to predict velocity, the vehicle speed and acceleration should be dispersed to describe the stochastic vehicle driving condition.…”
Section: B Microcosmic Velocity Prediction In On-board Vcumentioning
confidence: 99%
“…By 5G cellular network, data collected from route segments and vehicles could be rapidly sent to MEC servers [12], some control process could be manipulated in MEC servers instead of in VCUs, thus achieving the VEC control and realizing the VCU computation intensity. Some research in terms of the electric vehicle charging planning has been performed by the MEC methods, which shows dramatic improvement [13], [14]. The MEC based methods applied in VEC control to predict velocity profile, to the best knowledge of authors, are still quite seldom to be found in existing literatures.…”
Section: Introductionmentioning
confidence: 99%