Journal of Science and Transport Technology (JSTT) (ISSN: 2734-9950) under the publisher of University of Transport Technology (UTT) has been allowed to be annually published with the issues in English under the Document No. 399/GP-BTTTT dated 29th June 2021 by the Ministry of Information and Communication, Vietnam.

JSTT puts its efforts on gradually improving the quality of published articles and its online editorial system to meet international standards. It is considered as a prestigious forum for local and international scientists to exchange and publish new research results to serve the needs of scientific activities and application development in industry practices. To continue to affirm its prestige in the international integration, the Journal needs a great effort of contribution of domestic and international scientists.

JSTT is a free open access peer-reviewed scientific journal which publishs highly qualified original, review articles and technical notes in all aspects of science related to transport and construction. It publishs 4 issues per year, and covers the following areas but are not limited:

- Transport planning and traffic engineering
- Civil and structure engineering
- Construction materials
- Mechanical engineering
- Mechanics
- Geotechnical engineering
- Logistics and freight transport
- Construction economics and management
- Earth siences
- Environmental sciences
- Computer sciences
- Electricity, electronics, telecommunications
- Automotive engineering

Vol. 4 No. 2 (2024)

Published: 2024-06-30

Landslide Susceptibility Zoning: Integrating Multiple Intelligent Models with SHAP Analysis

Indra Prakash, Dam Duc Nguyen, Nguyen Thanh Tuan, Tran Van Phong, Le Van Hiep


Efficiency Evaluation of Using Water-Fuel Emulsion as Fuel for Automobiles

Hiep Ha Nguyen, Doan Cong Nguyen, Thanh Le Nguyen


Mechanical properties and microstructures of cement-treated soils: a review

Viet Quoc Dang, Vinh Ngoc Chau, Nguyen Thuan Thuyen , Hoang Anh Quan, Vu Trung Hieu, Dang The Vinh, Chandra Sekar Ganja, Lanh Si Ho


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NOTICE 3-2021


Special Issue: Big Data, AI, and Machine Learning in Transportation


It is predicted that Big Data will profoundly impact the transportation and logistics sector, which accounts for around 15% of global GDP. Researchers in the transportation field are no strangers to the challenges provided by big data. Model and observation resolution developments, together with the introduction of unique observation tools, have all led to the heightened sharpness of challenges. If big data difficulties are defined as the capacity to cost-effectively extend processing and storage in the face of ever-expanding data volumes and varieties and an ever-increasing requirement for speed, these concerns have existed from the inception of digital computing in the transportation field. Since models and observations generate an enormous amount of data, the requirement to extract the most value from it has become a new difficulty. There was no other alternative except to rely only on the cognitive capacity of humans until lately.

Artificial intelligence and machine learning (AIML) may be able to assist in solving the big data problem since AIML has made significant progress in recent years, routinely outperforming people in specialized cognitive tasks and often doing better than humans. To add to the intrigue, the fact that every transportation challenge has its own unique set of dynamics makes it even more fascinating. Similar to the rise of data, machine learning models improve as the amount and variety of data grow. There is a direct correlation between these two issues: if the data cannot be wrangled, processed, and stored using methodologies that are scalable and parallel in nature, machine learning will not be incredibly effective. Therefore, there is a crucial need to illustrate the advances that Big Data and AIML will bring to the transportation field in a realistic, quantifiable, and reproducible fashion.

This Special Issue proposes assessing the progress and investigating the synergy of tackling these two connected challenges in the transportation field. High-quality original research papers on "Big Data, AI, and Machine Learning in Transportation" are welcome.

NOTICE 2-2021


ISSN : 2734-9950 for Journal of Science and Transport Technology

We are pleased to inform that the Journal of Science and Transport Technology has got an International Standard Serial Number (ISSN: 2734-9950) which is an unique number used to identify an periodical journal. We welcome all authors and researches to submit their quality works and papers for consideration of publication in our journal.