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AADT Prediction Methodology

This documentation outlines the methodology involved in predicting AADT (Average Annual Traffic).

Overview​

AADT (Average Annual Daily Traffic) Prediction is an essential function within TES that estimates traffic volumes for locations where actual traffic counts are unavailable for a given year. The prediction process ensures that municipalities and transportation planners can make data-driven decisions about road design, safety measures, and infrastructure planning.

TES uses two primary methods for AADT prediction:

  1. By Location: Predicts AADT for a given location using historical traffic data.
  2. By Network: Estimates AADT at uncounted locations by leveraging geometric relationships among road segments and intersections.

AADT Prediction - By Location​

This method relies on past AADT values recorded at a specific location to estimate missing values for years when no count was conducted.

Process​

  1. Historical Data Collection: AADT values from previous years at the same location are gathered.
  2. Trend Analysis: A regression model (often linear) is applied to establish the trend in AADT growth or decline over time.
  3. Interpolation & Extrapolation: Missing values are estimated using the regression equation, filling gaps between counted years.

Example​

Where x represents the year.

By applying the equation, missing AADT values are estimated based on observed trends.

AADT Prediction - By Network​

This method predicts AADT for locations where no historical data is available by leveraging geometric relationships among road segments and intersections.

Process​

  1. Data Classification:
    • Counted Midblocks
    • Counted Intersections
    • Non-Counted Midblocks
    • Non-Counted Intersections
  2. Geometric Relationship Analysis:
    • AADT from counted midblocks and intersections is analyzed.
    • Non-counted locations are assigned AADT values based on their geometric positioning within the network.
    • If a road segment has no prior count, the system estimates its AADT based on adjacent midblocks and intersections.
  3. AADT Assignment: The predicted values are adjusted using statistical factors (e.g., functional classification, road hierarchy, and connectivity).

If two adjacent intersections have recorded AADT values, the missing midblock AADT is interpolated based on the network structure.