Passenger Vehicle Idling in Vermont: Year 1 Report

Sullivan, Jim; Dowds, Jonathan; Aultman-Hall, Lisa · 2012 · ROSA P / University of Vermont. Transportation Research Center

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This report presents the findings of Year 1 of a study conducted by the University of Vermont Transportation Research Center to quantify passenger vehicle idling behavior in Vermont. The research was motivated by the transportation sector’s status as the state’s largest source of greenhouse gas emissions and energy use. While existing laws restrict idling for school buses and vehicles in Burlington, there was a lack of empirical data on passenger vehicle idling, particularly regarding the distinction between discretionary idling (controlled by the driver, such as at trip starts or ends) and non-discretionary idling (caused by traffic or congestion). The study aimed to pilot-test instrumentation and analytical methods to accurately measure these behaviors, noting that self-reported idling times often underestimate actual occurrences. The methodology involved equipping vehicles with volunteer drivers with synchronized Global Positioning System (GPS) and On-Board Diagnostic (OBD) devices to record second-by-second data on position, speed, and engine performance. After evaluating various hardware options, the team selected the GeoLogger GPS device and the EaseDiagnostics MiniDL OBD device. Twenty volunteers participated in the Winter 2012 data collection phase, having previously participated in a Summer 2011 phase. Data was collected over 14-day periods to capture weekday and weekend driving patterns. The researchers developed a rigorous data processing pipeline using MATLAB to synchronize the GPS and OBD streams, correct for GPS signal acquisition delays, and identify Zero Speed Events (ZSEs). A key analytical innovation was the use of heading-change logic to distinguish discretionary idling events, such as parking at intermediate destinations, from non-discretionary stops in traffic. The report details the technical procedures for data cleaning, including the identification of questionable GPS records and the alignment of datasets based on speed correlations. It establishes the framework for categorizing idling events into spatial and non-spatial sets, allowing for the isolation of discretionary idling. The study successfully generated descriptive statistics on volunteer drivers and their idling patterns, providing a baseline for understanding seasonal variations in behavior. By distinguishing between idling types, the research provides a methodological foundation for assessing the potential fuel savings and emission reductions associated with reducing discretionary idling. The significance of this work lies in its contribution to evidence-based policy development for reducing transportation emissions in Vermont. By providing accurate, instrumented data rather than relying on self-reports, the study offers a more reliable basis for evaluating the effectiveness of anti-idling ordinances and eco-driving education programs. The distinction between discretionary and non-discretionary idling is crucial for policymakers, as it suggests that interventions targeting driver behavior (such as education) are appropriate for discretionary idling, while infrastructure improvements may be required for non-discretionary idling. This Year 1 report serves as a pilot validation of the methods, setting the stage for Year 2, which aims to expand the sample size and estimate total fuel consumption and carbon emissions attributable to discretionary idling across the state.

Key finding

The study successfully demonstrated a methodology for distinguishing discretionary idling from non-discretionary traffic idling using synchronized GPS and OBD data from 20 Vermont drivers.

Methodology

naturalistic

Sample size: 20

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• exposure measurement

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence
• Methodological Resource: dataset resource