Document Type

Conference Paper

Publication Date

2026

DOI

10.18260/1-2--60258

Publication Title

2026 ASEE Annual Conference & Exposition

Pages

6 pp.

Conference Name

2026 ASEE Annual Conference & Exposition, June 21-24, 2026, Charlotte, North Carolina

Abstract

Emerging transportation technologies are rapidly reshaping transportation systems and industry practice. However, most transportation undergraduate curricula still emphasize foundational topics such as geometric design, travel demand forecasting, pavements, and soil properties, typically delivered through lecture-centric instruction. While these subjects remain essential to the discipline, they do not fully reflect the pace of technological change or provide sufficient opportunities for experiential learning with modern tools and data. This gap limits students’ exposure to CV concepts and their ability to translate theory into practice.

Focusing on a key emerging technology, connected vehicles (CVs), this paper bridges the above gap by introducing a new gamified virtual learning platform that delivers fundamental, up-to-date CV knowledge to undergraduate students. The platform integrates: (i) a student-facing, task-guided graphical user interface (GUI) that requires no programming; (ii) four essential CV applications, namely Forward Collision Warning (FCW), Emergency Electronic Brake Light (EEBL), Blind Spot Warning (BSW), and Lane Change Warning (LCW), implemented with transparent, parameterized logic; and (iii) driver-response behavior modules calibrated to real-world evidence, enabling students to observe how heterogeneous human reactions propagate to system-level outcomes.

Technically, the platform couples a validated microscopic virtual traffic simulation engine, Simulation of Urban MObility (SUMO), with a modular CV applications layer, a driver behavior response layer, and a GUI for scenario design and rapid assessment. Students can select roadway types (freeways, urban arterials, local streets), traffic states (free-flow to congested), and CV market penetration to elicit hypothesis-driven exploration and engaged learning. The driver response modules (e.g., perception–reaction time distributions, deceleration profiles, gap-acceptance adjustments) were calibrated using real-world data collected from the New York City CV Pilot Deployment, ensuring that classroom experiments reflect realistic operating conditions rather than artificial assumptions. Visual encodings within the interface (including dynamic vehicle state and color changes when applications trigger) make otherwise abstract CV logic observable and more engaging.

We evaluated the platform with 31 undergraduate students via individual, in-person sessions using a pre/post test design. The pre-survey captured demographics, prior transportation knowledge, driving experience, and baseline familiarity with CV technologies. After guided interactions with the developed platform, students completed a post-survey on perceived knowledge gains, application-specific understanding, usability, and learning experience. Wilcoxon signed-rank tests and a linear mixed-effects model are employed to assess overall learning gains and heterogeneous effects across student backgrounds. Results show consistent, statistically significant improvements in self-reported understanding of CV applications, most notably FCW and BSW, and increased interest in transportation engineering. Students highlighted the application triggers plus well calibrated driver responses as the most instructive elements, crediting the platform’s visuals and parameter controls for clarifying cause-and-effect. By unifying a user-friendly GUI, implemented CV applications, and calibrated driver response behavior in a modular architecture, this platform provides a scalable pathway to embed experiential, data-driven learning into core transportation courses. This work was supported by the National Science Foundation under the IUSE: EDU program.

Rights

© 2026 American Society for Engineering Education.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2026 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference.

ORCID

0000-0003-2808-8852 (Yang), 0000-0002-8191-2786 (Xie)

Original Publication Citation

Shen, T., Yang, D., Sun, K., Yang, H., Xie, K., & Jeihani, M. (2026). IUSE: A gamified virtual learning platform for connected vehicle applications to enhance undergraduate transportation education [Conference paper]. 2026 ASEE Annual Conference & Exposition, Charlotte, North Carolina. https://doi.org/10.18260/1-2--60258

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