Description
In recent years, machine learning techniques (e.g. support vector machine (SVM), decision tree, random forest, etc.) and deep learning techniques (e.g. convolutional neural network (CNN), recurrent neural network (RNN), long-short term memory (LSTM), etc.) have been popularly applied into image recognition and time-series inferences for intelligent transportation systems (ITS). For instance, advanced driver assistance systems and autonomous cars have been developed based on AI techniques to perform forward collision warning, blind spot monitoring, lane departure warning systems, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. Autonomous vehicles can share their detected information (e.g., traffic signs, collision events, etc.) with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. However, the performance and efficiency of these techniques are big challenges for performing real-time applications.
The aim of this workshop is to focus on both original research and review articles on various disciplines of ITS applications, including particularly AI techniques for ITS time-series data analyses, ITS spatio-temporal data analyses, advanced traffic management systems, advanced traveler information systems, commercial vehicle operation systems, advanced vehicle control and safety systems, advanced public transportation services, emergency management services, electronic payment services, advanced information management services, information management services, vulnerable individual protection services, etc.
Topics of interest
This special issue aims to address, but not limited to, the following topics:
1. AI for ITS time-series and spatio-temporal data analyses
2. AI for advanced traffic management and safety, traveler information, commercial vehicle operation, advanced vehicle control and safety, and advanced public transportation systems
3. AI for emergency management, electronic payment, advanced information management, and vulnerable individual protection services
4. AI for image recognition
5. Applications and techniques in image recognition based on AI techniques for ITS
6. Applications and techniques in autonomous cars and ships based on AI techniques
7. AI for quality of service in VANET
8. AI for infrastructure management and congestion.
Connection to AAAI 2022 workshop
The AAAI is one of top conferences in AI. This special issue in Journal of Traffic and Transportation Engineering (English Edition) (JTTE) will accommodate extended versions of papers that will be presented in the AAAI 2022 workshop entitled “AI for Transportation” after further peer review process of JTTE.
Although papers presented at the AAAI 2022 workshop will be given priority, other papers are also welcome in the special issue.
Submission deadline
Submit your paper for the AAAI 2022 Workshop: AI for Transportation until the 12th of November (https://rail.fzu.edu.cn/info/1014/1064.htm).
Submit your paper for the JTTE special issue before June 30, 2022.
Guest Editors
Prof. Wenzhong Guo, fzugwz@163.com, Fuzhou University, Fuzhou, China
Prof. Chin-Chen Chang, alan3c@gmail.com, Feng Chia University, Taichung, Taiwan, China
Prof. Chi-Hua Chen, chihua0826@gmail.com, Fuzhou University, Fuzhou, China
Prof. Haishuai Wang, hwang@fairfield.edu, Fairfield University, Fairfield, USA
Prof. Cheng Shi, chengc_s@163.com, Xi’an University of Technology, Xi’an, China
Prof. Feng-Jang Hwang, feng-jang.hwang@uts.edu.au, University of Technology Sydney, Sydney, Australia
Dr. Ching-Chun Chang, c.c.chang.phd@gmail.com, National Institute of Informatics, Tokyo, Japan
For further questions about this special issue, please contact Guest Editors at chihua0826@gmail.com or JTTE editorial office at jtte2014@126.com.