Driven by new emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and edge computing, diversified mobility demands tend to be managed and fulfilled by intelligent and automated systems, often leveraging collective insights and distributed decision-making powered by Crowd Intelligence. This trend illustrates the evolution from Intelligent Transportation Systems (ITS) to Autonomous Transportation Systems (ATS), which can perceive user demands, learn decisive knowledge, and rearrange system supplies, as well as harness the wisdom of the crowd to adapt to real-time traffic patterns, optimize routing and scheduling, and personalize journey experiences.

  As ATS renovates the portfolios of ITS services, challenges arise in implementing a compatible and scalable system by adopting emerging technologies. It is essential to overcome these challenges to reach a new balance between auto-sensed demand and auto-organized supply. This leverages the collective sensing and computing power of distributed computing nodes and incorporates Crowd Intelligence principles to make use of big data while preserving individual privacy. Moreover, a novel cyber-physical system capable of simulating, validating, and optimizing system designs becomes critical for ATS as a sandbox to ensure the integrity and quality of autonomous and intelligent services to be implemented.

  This special issue aims to invite submissions of research/survey/application/demo papers related to ATS applications and theories in Crowd Intelligence. We welcome contributions exploring, but not limited to the following:

• The design principles, system architectures, and applications of ATS
• AI-enabled, edge-based, and federation-supported ATS
• Applications of multi-agent systems (MAS) and distributed reinforcement learning (DRL) in ATS
• Transportation cyber-physical systems designing and modeling
• Traffic flow modeling and optimization in the mix of human-driven vehicles (HDVs) and connected and automated vehicles (CA/AVs)
• Applications of big data-driven models for autonomous driving
• IoT and cloud computing applications in transport systems
• Traffic information detection and fusion based on Crowd Intelligence
• Crowd-based methods for human-vehicle interaction and environmental understanding
• Crowd-based fault detection and anomaly event warning mechanisms
• Modeling and control of connected, cooperative and autonomous driving
• Planning and cooperation for multiple automated vehicles based on evolutionary game
• Intelligent traffic signal control and cooperative intersections
• Autonomous intersection management for CV/AVs with coordination learning
• Crowd Science in urban air mobility (UAM)
• Innovative and emerging solutions for transportation system management (e.g., artificial intelligence, ChatGPT)

  International Journal of Crowd Science (IJCS) is an international, peer-reviewed open-access academic journal, which publishes inter-disciplinary research on crowd intelligence. It is indexed by Ei Compendex, Scopus, Inspec, DOAJ, etc. The authors are requested to submit their full research papers complying with the general scope of the journal. The submitted papers will undergo peer review process before they can be accepted. Notification of acceptance will be communicated as we progress with the review process.

SUBMISSION GUIDELINES

Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc.manuscriptcentral.com/ijcse with manuscript type as “Special Issue on Crowd Intelligence for Autonomous Transportation Systems: Modeling, Learning and Control”. Further information on the journal is available at: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9736195

IMPORTANT DATES

Deadline for submissions: January 30, 2025                           

Publication online (tentative): May 30, 2025

GUEST EDITORS

• Wei Huang, Sun Yat-sen University, China. E-mail: huangwei5@mail.sysu.edu.cn
• Ming Cai, Sun Yat-sen University, China. E-mail: caiming@mail.sysu.edu.cn
• Honghai Li, RIOH High and Technology Group, China. E-mail: honghai_1@126.com
• Li Li, Tshinghua University, China. E-mail: li-li@mail.tsinghua.edu.cn
• Zhengbing He, Massachusetts Institute of Techonology, United States. E-mail: he.zb@hotmail.com
• Zhishuo Liu, Beijing Jiaotong University, China. E-mail: zhsliu@bjtu.edu.cn