Knowledge Graphs and Machine Learning Techniques for Sustainable Transportation Systems
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 6036
Special Issue Editors
Interests: intelligent systems; open data; data science; data journalism; knowledge graphs; machine learning; semantic web
Special Issues, Collections and Topics in MDPI journals
Interests: research and developments in transport and mostly in algorithm and model development; mobility; intermodal transport and logistics as well as Data Science and Big Data at the transport domain
Interests: traffic management; transport systems management; use of telematics applications in: urban mobility and information services; combined transport; vehicle fleet management; operations research and in road safety
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The needs of modern life, rapid growth of population, urban sprawl, air pollution, and other environmental problems cause several issues in transportation systems, and intelligent approaches have been applied to resolve them in an efficient manner.
Intelligent approaches to improving and optimizing transportation-related services include unlocking hidden knowledge and patterns in increasingly spatiotemporal and crowdsourced information collected from various sources, such as mobile phone sensors, vehicle telemetric, Bluetooth-enabled devices, and Twitter.
Machine learning is one element of Artificial Intelligence, where computers can self-teach and improve their performance of specific tasks. Complementary to machine learning processes are knowledge graphs, which offer a way to model any domain’s data with the help of experts, interlink data, automate responses, and scale intelligent decisions. Specifically, knowledge graphs and machine learning include techniques for describing and analyzing transport data and extracting useful knowledge on traffic conditions and mobility behaviors.
This Special Issue aims to provide the most recent advances in knowledge graphs and machine learning techniques on intelligent transportation systems as well as to bring knowledge graphs and machine learning together to intelligent transportation system applications and extend their range of capabilities.
Topics of interest include but are not limited to the following:
- Applications that highlight the successful adoption of knowledge graphs in transport;
- ITS and traffic management;
- Development and utilization of knowledge graphs in transport;
- Knowledge graph frameworks for sustainable transportation;
- Data analysis of road traffic measurements;
- Mobility patterns and knowledge extraction;
- Traffic status prediction;
- Impact and use cases of open transport data.
Dr. Charalampos Bratsas
Dr. Josep-Maria Salanova Grau
Dr. Georgia Aifadopoulou
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- mobility patterns
- traffic management
- knowledge graphs
- intelligent transport systems
- traffic status
- open transport data
- machine learning techniques
