Special Issue "Machine Learning and Deep Learning for Transportation"

Submission Deadline: 30 March 2021 (closed)
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
  • Implementation of Multi-Object Recognition System for the Blind
  • Abstract Blind people are highly exposed to numerous dangers when they walk alone outside as they cannot obtain sufficient information about their surroundings. While proceeding along a crosswalk, acoustic signals are played, though such signals are often faulty or difficult to hear. The bollards can also be dangerous if they are not made with flexible materials or are located improperly. Therefore, since the blind cannot detect proper information about these obstacles while walking, their environment can prove to be dangerous. In this paper, we propose an object recognition system that allows the blind to walk safely outdoors. The proposed system can… More
  •   Views:105       Downloads:81        Download PDF

  • Analysis of Roadside Accident Severity on Rural and Urban Roadways
  • Abstract The differences in traffic accident severity between urban and rural areas have been widely studied, but conclusions are still limited. To explore the factors influencing the occurrence of roadside accidents in urban and rural areas, 3735 roadside traffic accidents from 2017 to 2019 were analyzed. Fourteen variables from the aspects of driver, vehicle, driving environment, and other influencing factors were selected to establish a Bayesian binary logit model of roadside crashes. The deviance information criterion and receiver operating characteristic curve were used to test the goodness of fit for the traffic crash model. The results show that: (1) the Bayesian… More
  •   Views:269       Downloads:231        Download PDF

  • Driving Pattern Profiling and Classification Using Deep Learning
  • Abstract The last several decades have witnessed an exponential growth in the means of transport globally, shrinking geographical distances and connecting the world. The automotive industry has grown by leaps and bounds, with millions of new vehicles being sold annually, be it for personal commuting or for public or commodity transport. However, millions of motor vehicles on the roads also mean an equal number of drivers with varying levels of skill and adherence to safety regulations. Very little has been done in the way of exploring and profiling driving patterns and vehicular usage using real world data. This paper focuses on… More
  •   Views:188       Downloads:131        Download PDF

  • Constructional Cyber Physical System: An Integrated Model
  • Abstract Artificial intelligence, machine learning, and deep learning have achieved great success in the fields of computer vision and natural language processing, and then extended to various fields, such as biology, chemistry, and civil engineering, including big data in the field of logistics. Therefore, many logistics companies move towards the integration of intelligent transportation systems. Only virtual and physical development can support the sustainable development of the logistics industry. This study aims to: 1.) collect timely information from the block chain, 2.) use deep learning to build a customer database so that sales staff in physical stores can grasp customer preferences,… More
  •   Views:306       Downloads:184        Download PDF

  • Design and Validation of a Route Planner for Logistic UAV Swarm
  • Abstract Unmanned Aerial Vehicles (UAV) are widely used in different fields of aviation today. The efficient delivery of packages by drone may be one of the most promising applications of this technology. In logistic UAV missions, due to the limited capacities of power supplies, such as fuel or batteries, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Thus, multiple unmanned vehicles with well-planned routes become necessary to minimize the unnecessary consumption of time, distance, and energy while carrying out the delivery missions. The aim of the present study was to develop a multiple-vehicle mission dispatch system… More
  •   Views:214       Downloads:160        Download PDF

  • 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
  •   Views:491       Downloads:385        Download PDF