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.


  • Transportation
  • Transport Infrastructure
  • Intelligent Systems
  • Smart City
  • Data Science
  • Industry 4.0
  • Data Mining with Big Data
  • Artificial Intelilgence
  • Machine Learning
  • Optimization Algorithms

Deadline: 30/11/2022

Chief Guest Editor:

Title: Dr.

Name: Hai-Bang Ly

Affiliation: Civil Engineering Department, University of Transport Technology, Hanoi 100000, Vietnam.



Research Interests: Materials; Structures; Data Science; Big Data Analytics; Machine learning; Transportation.