VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!

Scholarly Works, Virginia Tech Transportation Institute

Permanent URI for this collection

https://hdl.handle.net/10919/89575

Browse

Browsing Scholarly Works, Virginia Tech Transportation Institute by Author "Ahmadian, Mehdi"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Quantifying the effect of roadway, driver, vehicle, and location characteristics on the frequency of longitudinal and lateral accelerations
    Ali, Gibran; McLaughlin, Shane B.; Ahmadian, Mehdi (Pergamon-Elsevier, 2021-10-01)
    The purpose of this study is to understand and quantify the simultaneous effects of roadway speed category, driver age, driver gender, vehicle class, and location on the rates of longitudinal and lateral acceleration epochs. The rate of usual as well as harsh acceleration epochs are used to extract insights on driving risk and driver comfort preferences. However, an analysis of acceleration rates at multiple thresholds incorporating various effects while using a large-scale and diverse dataset is missing. This analysis will fill this research gap. Data from the 2nd Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was used for this analysis. The rate of occurrence of acceleration epochs was modeled using negative binomial distribution based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as the fixed effects and the driver identifier was used as the random effect. Incidence rate ratios were then calculated to compare subcategories of each fixed effect. Roadway speed category has the strongest effect on longitudinal and lateral accelerations of all magnitudes. Acceleration epoch rates consistently decrease as the roadway speed category increases. The difference in the rates depends on the threshold and is up to three orders of magnitude. Driver age is another significant factor with clear trends for longitudinal and lateral acceleration epochs. Younger and older drivers experience higher rates of longitudinal accelerations and decelerations. However, the rate of lateral accelerations consistently decreases with age. Vehicle class also has a significant effect on the rate of harsh accelerations with minivans consistently experiencing lower rates.
  • Thumbnail Image
    The Surface Accelerations Reference— A Large-Scale, Interactive Catalog of Passenger Vehicle Accelerations
    Ali, Gibran; McLaughlin, Shane; Ahmadian, Mehdi (IEEE, 2023-04)
    There is a need for a large-scale, real world, diverse, and context rich vehicle acceleration catalog that can be used to design, analyze, and compare various intelligent transportation systems. This paper fulfills three primary objectives. First, it provides such a catalog through the Surface Accelerations Reference, which is openly available as an interactive analytics tool as well as an open and downloadable dataset. The Surface Accelerations Reference statistically describes the driving profiles of about 3,500 individuals contributing 34 million miles of continuous driving data collected in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). These profiles were created by summarizing billions of longitudinal and lateral acceleration epochs experienced by the participants. Second, this paper introduces a standardized methodology for creating such a catalog so that similar acceleration profiles can be produced for other human cohorts or automated driving systems. Finally, the data are used to analyze the effect of roadway speed category on the rates of lateral and longitudinal acceleration epochs at various thresholds. It is observed that, for the median driver, the rates of epochs are up to three orders of magnitude higher on low-speed roads as compared to high-speed roads. This catalog will facilitate intelligent vehicle system designers to compare and tune their systems for safer driving experiences. It will also allow agencies with similar data to create comparable catalogs facilitating safety and behavioral comparisons between populations. Datasets: https://github.com/gibran-ali/surface-accelerations-reference.