Notice of Retraction: Intelligent Transportation System in Macao Based on Deep Self-Coding Learning

Li, Daming; Deng, Lizhen; Cai, Zhiming; Yao, Xiang

doi:10.1109/tii.2018.2810291

Cited by 68 publications

(34 citation statements)

References 8 publications

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“…A total of 12.12% of the identified articles were assigned to the cluster "intelligent transport logistics". Li et al (2018) introduce an intelligent transport system that combines information technology, data communication technology, electronic sensing technology, global positioning technology, geographical information system technology, computer processing technology, and system engineering technology to a real-time, accurate, efficient and intelligent transportation management system based on deep belief network models (DBN) and support vector regression classifier (SVR) [54]. Cheng et al (2017) discuss a fuzzy-group-based control for smart transportations, which reduces waiting time and improves the performance by up to 40% [55].…”

Section: Intelligent Transport Logisticsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

169

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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Section: Intelligent Transport Logisticsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

169

View full text Add to dashboard Cite

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“…Many of the research works have focused on providing transportation solutions related to the traffic management. For example, Li et al [11] applied the intelligent transportation system in Macao for enhancing the quality of traffic operations in the city. The authors used deep belief network and support vector regression to forecast the traffic characteristics.…”

Section: Smart Transportation and Energy Tradingmentioning

confidence: 99%

“…Using SoC pr k , the e av k is computed. If (e av i < e th i ), then p en k is calculated (line [10][11][12][13][14][15][16][17][18][19][20][21][22]. After this, the CSs compute their utility on the basis of e rt i and p en k and compare it with the previous instance.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

SURVIVOR: A blockchain based edge-as-a-service framework for secure energy trading in SDN-enabled vehicle-to-grid environment

2019

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Electric vehicles (EVs) have transformed the smart transportation sector by providing diverse energy management solutions to the smart grid. Energy trading among EVs and charging stations (CS) in a vehicle-to-grid (V2G) environment is one of the popular verticals in smart grid. However, processing the energy trading decisions at remote control centers lead to an increase in delay and network overhead. Apart from these issues, the security concerns while trading the energy in such an environment remain persistent. Therefore, to handle the aforementioned issues, this paper presents SURVIVOR: A Blockchain based Edge-as-a-Service Framework for Secure Energy Trading in software defined networking (SDN)-enabled V2G Environment. In the proposed framework, the energy trading decisions are processed closer to the location of EVs through edge nodes. Moreover, for securing the energy trading transactions, blockchain is used wherein the approver nodes are selected amongst all the present nodes on the basis of a utility function and are made responsible for validating the transactions. Once such nodes are selected, a consensus-based blockchain mechanism for secure energy trading in SDN-enabled V2G environment is presented. In this mechanism, edge nodes are responsible for generating proof-of-work puzzles. The proof-of-work is * Corresponding Author a unique hash value which is computed for each EV and the transactions for which the approver nodes compute the same proof-of-work for each EV are added in the blockchain. The complete scheme is backed by the SDN architecture to reduce the overall latency and increase the throughput of the smart transportation network. The results obtained prove that the proposed scheme is effective for trading the energy between EVs and CS while securing the underlying trading transactions using blockchain. Moreover, the communication and computation cost of the proposed scheme comes out to be small which proves that it can be used in real-world applications. The latency in the complete transportation sector is also greatly reduced by using the SDN-architecture.

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“…In the timing plan, the traffic sequence at the intersection is called the lane combination. A lane combination consists of two lane sequences, as in (2).…”

Section: B Timing Calculationmentioning

confidence: 99%

“…The intelligent transportation system provides effective support for solving traffic congestion. It integrates multiple disciplines in the field of information and aims to establish a full-coverage, real-time, accurate, and efficient integrated transportation and management system [1], [2].…”

Section: Introductionmentioning

confidence: 99%

An Indefinite Cycle Traffic Light Timing Strategy

Guo¹,

Lei²,

Ye³

2018

ijacsa

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Intelligent transportation signal control plays an important role in reducing traffic congestion and improving road capacity. The key of signal control is to adjust the traffic lights appropriately according to the traffic flow, which is an adaptive control. In this paper, we propose a new timing strategy. This strategy includes green time optimization and lane combination calculation. According to the real-time traffic flow, we optimize green time and calculate lane combination to adjust the cycle and then we can get the timing plan. The simulation results of random data and actual traffic data show that the strategy we proposed can increase traffic efficiency by more than 15% at intersections, reduce vehicle detention, and relieve traffic congestion.

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Notice of Retraction: Intelligent Transportation System in Macao Based on Deep Self-Coding Learning

Cited by 68 publications

References 8 publications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

SURVIVOR: A blockchain based edge-as-a-service framework for secure energy trading in SDN-enabled vehicle-to-grid environment

An Indefinite Cycle Traffic Light Timing Strategy

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