Special Issue "Machine Learning and Deep Learning for Transportation"

Submission Deadline: 30 March 2021
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Guest Editors
Prof. Chi-Hua Chen, Fuzhou University, China
Dr. Feng-Jang Hwang, University of Technology Sydney, Australia
Dr. Chunjia Han, University of Greenwich, United Kingdom
Prof. Xiao-Guang Yue, European University Cyprus, Cyprus
Dr. K. Shankar, Alagappa University, India
Prof. Fangying Song, Fuzhou University, China


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 machine learning and deep learning 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 the 5th generation mobile networks) for cooperation. However, the performance and efficiency of these techniques are big challenges for performing real-time applications.


Therefore, several optimization techniques (e.g. gradient descent algorithm, Adam optimization algorithm, particle swarm optimization algorithm, etc.) have been proposed to support deep learning algorithms in finding faster solutions. For example, the gradient descent method is one of the most popular optimization techniques to quickly seek the optimized weight sets and filters of CNN for image recognition. The ITS applications based on these image recognition techniques (e.g., autonomous cars, augmented reality navigation systems, etc.) have gained increasing attention, and the hybrid approaches typical of mathematics for engineering and computer science (e.g. machine learning, deep learning, and optimization techniques) can be investigated and developed to support a variety of ITS applications.


The aim of this Special Issue is to focus on both original research and review articles on various disciplines of ITS applications, including particularly machine learning, deep learning and optimization 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.


Potential topics include, but are not limited to, the following:

• Machine learning, deep learning, and optimization techniques for ITS time-series and spatio-temporal data analyses

• Machine learning, deep learning, and optimization techniques for advanced traffic management and safety, traveler information, commercial vehicle operation, advanced vehicle control and safety, and advanced public transportation systems

• Machine learning, deep learning, and optimization techniques for emergency management, electronic payment, advanced information management, and vulnerable individual protection services

• Machine learning, deep learning, and optimization techniques for image recognition

• Applications and techniques for image recognition based on machine learning and deep learning for ITS

• Applications and techniques for autonomous cars and ships based on machine learning and deep learning

• Machine learning, deep learning, and optimization techniques for quality of service in VANET

• Machine learning, deep learning, and optimization techniques for infrastructure management and congestion


Warm reminder: Please select Special Issue: Machine Learning and Deep Learning for Transportation when you submit your article in IASC submission system

• Machine learning
• Deep learning
• Convolutional neural network
• Recurrent neural network
• Intelligent Transportation

Published Papers
  • Parallel Equilibrium Optimizer Algorithm and Its Application in Capacitated Vehicle Routing Problem
  • Abstract The Equilibrium Optimizer (EO) algorithm is a novel meta-heuristic algorithm based on the strength of physics. To achieve better global search capability, a Parallel Equilibrium Optimizer algorithm, named PEO, is proposed in this paper. PEO is inspired by the idea of parallelism and adopts two different communication strategies between groups to improve EO. The first strategy is used to speed up the convergence rate and the second strategy promotes the algorithm to search for a better solution. These two kinds of communication strategies are used in the early and later iterations of PEO respectively. To check the optimization effect of… More
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