IET Intelligent Transport Systems

Aims & Scope

IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures.

The scope of the journal includes the following: Sustainable Traffic Solutions; Deployments with enabling technologies; Pervasive Monitoring Applications; Demonstrations and evaluation; Economic and behavioural analyses of ITS services and scenarios; Data Integration and analytics; Information collection and processing; Image processing applications in ITS; ITS aspects of electric vehicles; Autonomous Vehicles; Connected Vehicle Systems; In-vehicle ITS, safety and vulnerable road user aspects; Mobility as a Service Systems; Traffic management and control; Public transport systems technologies; Fleet and public transport logistics; Emergency and incident management; Demand management and electronic payment systems; Traffic related Air Pollution Management; Policy and institutional issues; Interoperability, standards and architectures; Funding scenarios; Enforcement; Human machine interaction; Education, training and outreach.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Engineering, Environmental Science and Social Sciences, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Engineering, Environmental Science and Social Sciences, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• LSTM network: a deep learning approach for short‐term traffic forecast

  Citation: 1470

  Authors: Zheng, Weihai, Xingming, Peter C. Y., Jingmeng

• Reinforcement learning-based multi-agent system for network traffic signal control

  Citation: 451

  Authors: I., C., T., A.G.

• Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning

  Citation: 255

  Authors: Seyed Sajad, Michael, Enda

• Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach

  Citation: 230

  Authors: Da, Mansur R.

• Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

  Citation: 217

  Authors: Chao, Mingjun, Weida, Kaijia, Changle

• Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

  Citation: 217

  Authors: Sang‐Joong, Heung‐Sub, Wan‐Young

• Deep learning methods in transportation domain: a review

  Citation: 214

  Authors: Hoang, Le‐Minh, Tao, Chen

• Robust control of heterogeneous vehicular platoon with uncertain dynamics and communication delay

  Citation: 209

  Authors: Feng, Shengbo Eben, Yang, Dongsuk

• Deriving origin–destination data from a mobile phone network

  Citation: 206

  Authors: N., J.P., F.G.

• Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences

  Citation: 193

  Authors: H., Y., B., S.