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IEEE Open Journal of Intelligent Transportation Systems is a peer-reviewed journal published by IEEE, covering Autonomous Vehicle Technology and Safety, Traffic control and management, Traffic Prediction and Management Techniques. Over its lifetime it has published 506 papers, which have received 5,002 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Open Journal of Intelligent Transportation Systems, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Open Journal of Intelligent Transportation Systems style. No sign-up. |
| Publishes research in | Autonomous Vehicle Technology and Safety Traffic control and management Traffic Prediction and Management Techniques Transportation Planning and Optimization Vehicular Ad Hoc Networks (VANETs) |
| ISSN | 2687-7813 |
| Citation impact (2-yr) | 2.89 |
| h-index | 32 |
| i10-index | 174 |
| Article processing charge | $1,750 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Technical University of Munich |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
In cooperative, connected, and automated mobility (CCAM), the more automated vehicles can perceive, model, and analyze the surrounding environment, the more they become aware and capable of understanding, making decisions, as well as safely and efficiently executing complex driving scenarios. High-definition (HD) maps represent the road environment with unprecedented centimetre-level precision with lane-level semantic information,…
This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real…
Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using…
This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on…
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields…
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