This project addressed the challenge of accurately quantifying extended idling in heavy-duty vehicles (HDVs), a significant contributor to on-road nitrogen oxide (NOx) and particulate matter (PM2.5) emissions. The U.S. EPA’s MOVES model currently uses national averages for idling activity, which fail to reflect local patterns and operational realities. To bridge this gap, the study integrated rich, high-resolution GPS and ELD telematics data from EROAD with other probe datasets (INRIX, ATRI, StreetLight, WEJO) and TxDOT in-house sources.

The work began with a comprehensive literature review, covering two decades of idling research from TTI and other agencies, identifying both methodological advances and persistent gaps in real-world data capture. The project then evaluated each available dataset for spatial/temporal coverage, vehicle representation, and ability to support location-specific idling estimates.

A multi-stage methodology was developed to:

1. Map and quantify truck parking capacity – compiling and validating public and private parking facility inventories, including amenities, county locations, and idling restrictions, verified through GIS analysis and direct contact.
2. Determine idling factors – extracting occupancy and idling patterns from prior studies and scaling them with fresh EROAD data. Bayesian optimisation and machine learning models simulated idling behaviour by facility type, road class, and parking space availability.
3. Assess APU availability and use – cross-referencing manufacturer data and EROAD activity records with the age profile of the Texas long-haul fleet.

EROAD’s telematics data proved uniquely valuable due to its mandated HOS compliance tracking, enabling precise identification of idling durations, stop locations, and APU usage. The resulting outputs provide seasonally- and time-of-day-specific idling profiles for all Texas counties, broken down by facility type.

The study’s recommendations call for continued use of telematics data—especially EROAD’s—in combination with advanced modelling to support more accurate emissions inventories, regulatory planning, and targeted idling reduction strategies. This framework can be replicated for other states or adapted internationally where similar telematics partnerships exist.

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