Intelligent Transportation Analytics (ITA)

The 37th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2024), Hradec Kralove, Czech Republic, July 10 – July 12, 2024


Enhancing public transport in smart cities, especially in disaster-prone regions like ASEAN countries, is a multifaceted challenge that demands a comprehensive approach. The key to success lies in leveraging data-driven insights from disciplines such as mathematics, statistics, data mining, and machine learning to optimize transportation systems. By integrating smart technologies, ensuring disaster resilience, and fostering interdisciplinary collaboration, cities can create efficient and resilient transport networks that prioritize safety, sustainability, and the needs of the public. This involves policy formulation, funding allocation, and public engagement, all while harnessing the power of technology and data analysis to create more accessible, environmentally friendly, and responsive transportation solutions for urban populations.


The special session on Intelligent Transportation Analytics is dedicated to showcasing innovative research that applies principles and methodologies drawn from statistics, data mining, machine learning, and artificial intelligence to address tangible transportation challenges. This session welcomes contributions spanning a wide array of subjects, spanning from theoretical findings to pragmatic applications, and from academic investigations to industrial implementations. The topics of particular interest encompass, but are not confined to the following:

  • Visionary papers on transportation systems related to Society 5.0/Industry 4.0 applications 
  • Models to predict traffic congestion 
  • Distributed frameworks to discover knowledge from big transportation data
  • Mining transportation data streams
  • Mining uncertain transportation data
  • Machine learning/Deep learning/Data Mining/Statistical analysis of transportation data
  • Optimizing machine learning algorithms to predict traffic congestion effectively
  • User interfaces to visualize transportation systems
  • Multimodal analytics on transportation data
  • User Audio and Video interfaces
  • Multimodal analytics on transportation data
  • Case studies

Important dates

Paper submission:December 15, 2023 February 19, 2024
Final Notification:January 31, 2024 March 15, 2024
Registration:April 10, 2024
Camera Ready Copy:April 10, 2024
Conference Sessions:July 10 – 12, 2024


  • Prof. Rage Uday Kiran, The University of Aizu, Fukushima, Japan
  • Penugonda Ravikumar, Rajiv Gandhi University of Knowledge Technologies – RK Valley Campus, Kadapa District, Andhra Pradesh – 516330

Paper Submission

The conference proceedings will be published by Springer in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) series. A paper will be accepted either as a long or as a short paper. Long papers will be allocated 12 pages while short papers will be allocated 6 pages in the proceedings.

Papers must be written by using the Springer template ( The submissions will go through a double blind review for originality and scientific quality.


Prof. Rage Uday Kiran: uday.rage[a]

Organizers Bio:

R. Uday Kiran is currently working as an Associate Professor at the University of Aizu, Aizu Wakamatsu, Fukushima, Japan. He also works as a researcher at the University of Tokyo, Tokyo, Japan.  He received his Ph.D. degree in computer science from the International Institute of Information Technology, Hyderabad, Telangana, India.  He has published over 90 papers in refereed journals and international conferences, such as CIKM, EDBT, SIGSPATIAL, SSDBM, IEEE-FUZZ, IEEE-BIGDATA, ICONIP, PAKDD, DASFAA, and DEXA. He served as publication co-chair for DASFAA 2022 and publicity co-chair for PAKDD 2021. He is currently serving as the publicity co-chair for ICDM 2022.

Penugonda Ravikumar is currently working as an Assistant Professor in the Computer Science and Engineering department at IIIT – RK Valley, which is affiliated with Rajiv Gandhi University of Knowledge Technologies, located in Andhra Pradesh, India. His academic journey includes a Ph.D. in Computer and Information Systems from the University of AIZU in Japan, a master’s degree in computer science and automation from the Indian Institute of Science in Bangalore, India, and a bachelor’s degree in computer science and engineering from Sri Venkateshwara University College of Engineering. His research interests encompass a wide range of areas, including data mining, air pollution data analytics, traffic congestion data analytics, recommender systems, and time series classification. He has made significant contributions to the academic community through publications in respected journals such as IEEE Access, Applied Intelligence, and Electronics, as well as presentations at prestigious international conferences, including IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Big Data (IEEE Big Data), IEEE International Conference on Data Science and Advanced Analytics (DSAA), Asian Conference on Intelligent Information and Database Systems (ACIIDS), IEEE Symposium on Computational Intelligence and Data Mining (CIDM), International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), International Conference on Database and Expert Systems Applications (DEXA), International Conference on Soft Computing and Machine Intelligence (ISCMI), and Big Data Analytics (BDA).