Learning an Alternative Car-Following Technique to Avoid Congestion with an Instructional Driving Simulator

Antonio Lucas-Alba, Sharona T. Levy, Oscar M. Melchor, Ana Zarzoso-Robles, Ana M. Ferruz, Maria T. Blanch, Andres S. Lombas

Research output: Contribution to journalArticlepeer-review

Abstract

This paper addresses the problem of traffic congestion through a learning perspective, highlighting the capabilities of Information and Communication Technologies to transform society. Recent physical and mathematical analysis of congestion reveals that training drivers to keep a safe distance systematically contributes to the emergence and maintenance of interference congestion (so-called phantom traffic jam). This paper presents the WaveDriving Course (WDC), a simulated learning environment designed to help drivers progress from the traditional Drive-to-keep-Distance (DD) technique to a new car-following (CF) principle better suited for wave-like traffic, Drive-to-keep-Inertia (DI). The WDC is based on the ordinary knowledge of the driver (e.g., going through a series of traffic lights), and presents this situation in terms of two possible simultaneous behavioral strategies. The driver has the opportunity to verify that it is possible to achieve the same objective with different consequences. Finally, the WDC checks to what extent this learning generates transfer patterns in the analogous case of CF. The paper focuses on results concerning the first WDC module: the traffic-light analogy. Forty-two participants followed the whole learning procedure for about 30 min. An evaluative CF test was administered before and after visioning the tutorial and practicing on the simulator. Overall, transference from this traffic-light analog to the CF situation (posttest) was successful. Results confirm the adoption of the expected DI strategies (speed variability decreased, distance and distance variability to leader increased, fuel consumption decreased, platoon elongation decreased etc.). The need to improve the WDC teaching of the appropriate CF distance is discussed.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Learning Technologies
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Automobiles
  • Behavioral sciences
  • Devices for learning
  • educational simulation
  • Interference
  • Mathematical models
  • Safety
  • self-assessment technologies
  • traffic congestion
  • Training
  • Vehicles

ASJC Scopus subject areas

  • Education
  • Engineering (all)
  • Computer Science Applications

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