Journal of Science and Transport Technology (JSTT) (E-ISSN: 2734-9950) under the publisher of University of Transport Technology (UTT) has been granted permission by the Ministry of Information and Communication, Vietnam, under Document No. 399/GP-BTTTT dated June 29, 2021, to publish issues in English.
JSTT is dedicated to continuously enhancing the quality of its published articles and online editorial system to meet international standards. It serves as a prestigious platform for local and international scientists to exchange and publish new research findings, supporting scientific advancements and industry applications. In its pursuit to solidify its international standing, the Journal is actively seeking contributions from domestic and international scientists.
JSTT is a peer-reviewed scientific journal specializing in the field of construction, covering the following areas: building and industrial construction; bridge and road engineering; coastal, offshore, and hydraulic engineering; materials science; mechanical engineering; architecture and urban planning; economics and management; environmental engineering; natural sciences; and information technology. Through continuous development in both quantity and quality, the Journal has steadily established itself as a premier scientific and technological publication in the field of civil engineering construction; applied and natural sciences.
JSTT publishes high-quality original research articles, review articles, and technical notes covering various aspects of science and technology, particularly focusing on infrastructure development. It encompasses the following areas, with a scope that extends beyond these:
- 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 sciences
- Environmental Sciences
- Computer sciences
- Electricity, electronics, telecommunications
- Automotive engineering
Vol. 5 No. 1 (2025)
Published: 2025-03-30
2025-02-05
We are pleased to inform that the Journal of Science and Transport Technology (JSTT) (E-ISSN: 2734-9950) of the University of Transport Technology permitted by the Ministry of Information and Communications with License for press activities No 399/GP-BTTTT date 29th June 2021, has finally been accepted and indexed in SCOPUS. Please find the link below for scopus progress and acceptance: https://suggestor.step.scopus.com/progressTracker/?trackingID=E0847093E172D6F42021-11-16
Special Issue: Big Data, AI, and Machine Learning in Transportation
Summary
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.