Aims & Scope

The IEEE Open Journal of Intelligent Transportation Systems covers theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS), defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. Topics of consideration are:

    Big data analytics, machine learning, AI applications
    Communication (inter-vehicle, vehicle-to-roadside)
    Computers (hardware, software)
    Control (adaptive, fuzzy, cooperative, neuro)
    Decision Systems (expert systems, intelligent agents)
    Systems (engineering, architecture, evaluation)
    Information Systems (databases, data fusion, security)
    ITS Energy and Environment
    ITS, freight and logistics
    ITS in Developing Countries
    Human-Machine Interfaces
    Imaging & Real-Time Image Analysis
    Privacy & security of ITS
    Sensors (infrastructure and vehicle-based)
    Simulation (continuous, discrete, real-time)
    Signal Processing
    Smart Public Transport Systems
    Societal implications of ITS
    Standards
    Reliability & Quality Assurance
    Technology Forecasting & Transfer
    ​Traffic Flow Modelling and Control

Special Issue on Transport Research Supported by Remote Sensing Data
Submission Date: 2024-10-31

In recent years, the integration of satellite and Unmanned Aerial Vehicle (UAV) technologies has
become instrumental in advancing transportation research. These technologies provide unmatched
capabilities in data acquisition and analysis, offering a comprehensive, multi-dimensional view of
transportation systems. Satellite technology is pivotal for broad-area coverage and consistent data
collection, crucial for extensive transport studies. Its applications in transportation research include
monitoring traffic flows and ship movements over large areas, as well as assessing land use changes
due to transportation developments. Conversely, UAVs offer localized information, complementing
satellite data. They are especially valuable for detailed inspections of transportation infrastructure,
such as roads, bridges, and ports, playing a critical role in traffic and vessel monitoring, accident
analysis, and rescue operations. The use of UAV technology, in synergy with Building Information
Modeling (BIM), can further enhance the safety performance of transportation systems through more
efficient and accurate data collection. Together, the synergistic integration of satellite and UAV data
provides a multi-scale approach to transportation research, facilitating comprehensive studies from
macro-level planning and policy-making to micro-level traffic and maritime analysis.

We invite submissions that explore the innovative integration and application of satellite and UAV
technologies in various aspects of transport research, including but not limited to:
• traffic flow analysis and congestion studies
• maritime surveillance and traffic management
• infrastructure monitoring and maintenance
• advanced data processing techniques for satellite and UAV imagery
• use of satellite and UAV in roadway/maritime accident analysis and response
• UAV and building information modelling

Submission Deadline: October 31st, 2024

Guest Editors

Dr. Yajie Zou
Email: yajiezou@hotmail.com
Department of Traffic Engineering, College of Transportation Engineering, Tongji University,
Shanghai 201804, China
Research Interests: traffic monitoring; transportation data mining; statistical analysis; traffic safety

Dr. Xinqiang Chen
Email: chenxinqiang@stu.shmtu.edu.cn
Institute of institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai
201306, China
Research Interests: video data-driven intelligent transportation environment perception and
understanding; large-scale transportation data analysis (traffic flow data, AlS, etc.); smart
ship/autonomous port

Dr. Salvatore Antonio Biancardo
Email: salvatoreantonio.biancardo@unina.it
Department of Civil, Construction and Environmental Engineering, University of Naples Federico II,
80125 Naples, Italy
Research Interests: building information modeling; data mining; intelligent transportation systems

Dr. Octavian Postolache
Email: opostolache@lx.it.pt
Instituto de Telecomunicações, Universidade de Aveiro Campus Universitário de, R. Santiago, 3810-
193 Aveiro, Portugal
Research Interests: smart sensors; automated measurement systems; artificial intelligence;
biomedical sensors; intelligent transportation

Dr. John E. Ash
Email: ashjn@ucmail.uc.edu
Department of Civil and Architectural Engineering and Construction Management University of
Cincinnati, Cincinnati, OH, 45219, USA
Research Interests: traffic safety, traffic monitoring, statistical analysis, driver behavior

Special Issue on Safety-oriented Perception Systems for Intelligent Vehicles and Transportation Systems
Submission Date: 2024-12-31

Intelligent and autonomous vehicles have gained significant traction in recent years. Intelligent vehicles have the potential to reduce transportation-related accidents and improve efficiency, making transportation safety a top priority. As one of the most important systems of intelligent vehicles, perception system provides the vehicle with vital information about its surroundings.

Perception systems enable the vehicle to perform functions such as localization, object detection, tracking, and trajectory
prediction, helping the vehicle determine its absolute position on the map and its relative position with other vehicles, and
allowing it to understand the presence of other traffic participants, which enables a wide range of applications, including
advanced driver assistance systems and autonomous driving. To generate a more comprehensive, reliable, and robust
perception of the vehicle's surroundings, multi-sensor fusion-based perception is gaining increasing attention from academia and industry. Sensors such as radar, LiDAR, cameras, GNSS, and other environmental sensors can significantly enhance the performance of the perception system. Additionally, leveraging Vehicle-to-Everything (V2X) techniques, cooperative perception enables even more potential applications. By enabling intelligent vehicles and road-side infrastructures to share information from various sensors through a cooperative network, each agent in the system can sense out-of-range surroundings and obtain more accurate and robust perception results. This cooperative approach enhances the safety of transportation systems, making them more reliable and efficient. However, there are still a lot of challenges for perception systems in real application, such as complicated driving conditions, sensor failure, signal delay, and so on. In this perspective, the objective of this special issue is to gather the recent advancing research and industrial implementation on both ego-vehicle perception systems and cooperative perception systems. The topics of interest include, but are not limited to:

➢ Object detection algorithms
➢ Object tracking algorithms
➢ Trajectory prediction algorithms
➢ Multi-sensor fusion integrated localization
➢ SLAM techniques
➢ Cooperative perception based on V2V, V2I and V2X
➢ Sensor fault detection and isolation for perception systems
➢ Multi-sensor calibration for perception systems
➢ Testing and evaluation for perception systems

Submission deadline: December 31, 2024.
Publishment: Papers will be published upon acceptance and available through early access on IEEE Xplore.
Papers can be submitted at: https://mc.manuscriptcentral.com/oj-its, choose submission type: Special Section

Guest editors:
Dr. Lu Xiong, Professor, Tongji University, China (xiong_lu@tongji.edu.cn)
Dr. Letian Gao, Postdoctoral Researcher, University of California, Los Angeles (UCLA), USA (letiangao@g.ucla.edu)
Dr. Xin Xia, Research Scientist, University of California, Los Angeles (UCLA), USA (x35xia@g.ucla.edu)
Dr. Chuchu Fan, Assistant Professor, Massachusetts Institute of Technology (MIT), USA (chuchu@mit.edu)
Dr. Rui Liu, Associate Professor, Chang’an University, China (ruiliu@chd.edu.cn)