Classical or New Paradigms in Transportation Research Technologies

TOPICAL COLLECTIONS

Guest Editor

Jibo He

Tsinghua University, Department of Psychology, School of Social Sciences; Eye Transport Inc.

Dr. Jibo He, BA & BS from Peking University in China, MSc & Ph.D. in Psychology from the University of Illinois in the USA. Dr. He is a fellow of the National Academy of Inventors, a former university director of the Federal Aviation Administration Center of Excellence, and a tenured professor at Wichita State University. Dr. He has published in the areas of driving simulators, driver fatigue, driver distraction, biomathematical models of fatigue, flight simulators, eye tracking, user experience, and virtual reality. Dr. He is currently an associate professor at Tsinghua University, the Chief Scientist of Eye Transport Inc., and a consultant for BMW, Boeing, Huawei, China Eastern Airlines, and Apple.

Collection Overview

Technical obstacles, non-standardization of paradigms, and data analysis methods have harmed the implementation and replicability of transportation research. In addition, new emerging technology trends, such as autonomous vehicles and unmanned aerial vehicles (UAV), bring brand new research questions and opportunities for transportation researchers.

To overcome technical research obstacles, improve replicability and ecological validity, and answer new research questions, we propose a method collection titled "Classic or New Paradigms in Technologies for Transportation Research" just at the right time. This collection encourages several types of submissions to meet this important goal for our transportation research field:

1) All subfields of transportation research are welcome and highly appreciated, such as driving (mainly autonomous vehicles), general, civil, and military aviation, unmanned aerial vehicle (UAV), railway, spaceflight, sailing, and submarine.

2) In order to build standard or verifiable protocols for transportation research and instruction, traditional classic and open-source paradigms, software, and data analysis approaches are invited.

3) New paradigms and data analysis methods are welcome to address emerging research questions, such as take-over requests for autonomous vehicles and remote controls for unmanned aerial vehicles (UAV) and robots.

We hope this collection can emerge as another excellent example, like the Lane Change Task by Mattes. We encourage complete and open-source solutions in simulation, analysis, and visualizations for transportation research, such as but not limited to the classic car-following task, the SAGAT paradigm, meta-analysis, or recent meta-meta-analysis.