Publication Date

1-2-2026

Document Type

Article

Organizational Units

College of Natural Science and Mathematics, Geography and the Environment

Keywords

Transportation, Medical risk factors, Roads, Data visualization, Acceleration, Decision making, Machine learning, Learning

Abstract

Understanding human driving decisions is crucial for intelligent transportation research. Most existing studies focus on individual vehicles in limited contexts, which restricts broader applicability of results. Leveraging Vehicle-to-Everything (V2X) infrastructure, this study introduces a machine learning framework to model driving actions and detect outliers across diverse environments. This approach features a semantically enabled clustering method that groups similar driving behaviors based on speed and actions. It also adds a time-series learning model to identify typical driving behaviors across various contexts, thereby enabling detection of abnormal driving actions. A suite of visual tools has been developed to help interpret driving patterns, and a case study using six months of data from a V2X pilot project in Tampa, Florida, demonstrates the framework’s effectiveness in modeling human driving decisions. It also highlights discrepancies between context-appropriate driving behaviors and actual human actions to improve safety and efficiency for transportation planners and individual drivers.

Copyright Date

1-2-2026

Copyright Statement / License for Reuse

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights Holder

Xuantong Wang, Jing Li, and Jecca Bowen

Provenance

Received from PLOS

File Format

application/pdf

Language

English (eng)

Extent

22 pgs

File Size

3.0 MB

Publication Statement

Copyright is held by the Authors. User is responsible for all copyright compliance. This article was originally published as:

Wang, X., Li, J., & Bowen, J. (2026). A visualization-supported, hierarchical, action-learning model for driving behavior in a V2X environment. PLoS One 21(1), e0336268. https://doi.org/10.1371/journal.pone.0336268

Publication Title

PLoS One

Volume

21

Issue

1

First Page

e0336268

ISSN

1932-6203

PubMed ID

41481747

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