ARC Commercial Vehicle and Truck Models

Agency: 
Atlanta Regional Commission (ARC)
Mode: 
Highway
Goals and Objectives: 

These models are part of ARC’s broader, trip-based passenger plus freight regional (multi-county) demand modeling process which addresses US EPA’s air quality mandates, as well as regional traffic congestion issues. The freight modeling component also provides for the analysis of truck only toll (TOT) lanes.

Innovations: 

Truck class specific traffic counts are used as a basis for synthesizing a truck trip table. An innovative approach makes use do a logit model to estimate the percentage of commercial truck trips, calibrated to a set of 2005 commercial truck counts. The method also provides a systematic calibration adjustment that helps the model to achieve a higher accuracy of assigned truck volumes on a link-by-link basis. An adaptable traffic assignment routine is then used to systematically compare reported traffic counts to model assigned volumes and then use this comparison to adjust the starting trip table for each O/D pair.

Overview and Description: 

•    Truck Trip Generation: Separate linear regression equations are developed to estimate Heavy and Medium Size Truck trips, based on zonal employment in industrial, retail and commercial industry classes, and number of households in a zone, in each case adjusted by one of 7 different area type factors, with heavy truck trips multiplied by a factor of 3 in zones known to contain high truck traffic volumes. The external share of these total trip ends is then modeled as a function of a zone’s highway network distance to a cordon count location: so that   zones that are closer to the edge of the modeled region will generally have a higher share of external trips than other zones. At these external (cordon) stations, truck trips are then further split into external vs. through truck trips, based on facility type (with special values for specific Interstate cordons). 
•    Truck Trip Distribution/Time of Day Modeling: Based on past empirical (trip survey) data and expert judgment, a negative exponential travel time decay model is used to distribute internal to the region truck trips, while a power function was used to distribute external truck trips. Empirical evidence from past studies is used to further disaggregate this truck trips matrix by time of day (AM peak, midday, PM peak, night).
•    Truck Traffic Assignment: The ARC traffic assignment model incorporates several advanced features relating to the assignment of truck trips, including:

  1. separate assignments by time period
  2. coding of truck-prohibited links
  3. separate impedance calculation for trucks, incorporating tolls at a higher value of time than for passenger cars
  4. assigning trucks to their own path and maintaining the volumes separately on the output network
  5. separate loading of through trips

Passenger car equivalents (PCEs) of 1.5 and 2.0 are applied to Medium and Heavy Truck trips respectively, to capture their effects on within-region traffic congestion. In addition, the assignment method included two untypical features: a special truck penalty on one particular link and a technique to assign some heavy trucks to a path that does not go inside the I-285 (circumferential beltway) perimeter. This adaptable assignment process produces a new vehicle trip table that is adjusted to produce a table that matches the external truck counts “fairly closely”.

Spatial Detail: 

The modeling is applied to a 20 county region, within which 46 higher than average truck freight generating/attracting zones (based on truck trips per employee) are given special attention (see Figure 1).
   
Figure 1. Truck Zones

Mode Detail: 

Truck: disaggregated into Heavy (FHWA truck classes F8 – F13), Medium (FHWA truck classes F4-F6) and Commercial (includes passenger cars, light trucks, vans, and SUVs that are used for business purposes).

Commodity Detail: 

The focus is on truck trips. Commodity flows are aggregated into 3 truck size classes.

System Platform: 

The ARC model set is developed in Cube Cluster, using the TP+/Cube scripting language with an interface to several Fortran programs. A user interface to the Atlanta travel demand modeling system uses Viper’s Macro Processor Statements, integrated into the TP+ script.

Data Sources: 

•    Inputs:  The modeling uses truck counts from approximately 2,800 locations throughout the ARC 20-county modeled area for 2000, supplemented by a much smaller sample of commercial vehicle counts stratified by functional class group (freeway, arterial, collector) and area type (urban, suburban, rural).conducted during summer and fall of 2005. Other inputs are county and local area industrial  employment and household data, the regional highway network database, and various trip generation adjustment, and spatial interaction model (distribution model) parameters and friction factors from pervious (some external to ARC) truck trip modeling efforts.
•    Outputs: Truck to O/D matrices and network link specific truck trip counts by truck class and PCEs.

Example Inputs & Outputs: 

•    Inputs:  The modeling uses truck counts from approximately 2,800 locations throughout the ARC 20-county modeled area for 2000, supplemented by a much smaller sample of commercial vehicle counts stratified by functional class group (freeway, arterial, collector) and area type (urban, suburban, rural).conducted during summer and fall of 2005. Other inputs are county and local area industrial  employment and household data, the regional highway network database, and various trip generation adjustment, and spatial interaction model (distribution model) parameters and friction factors from pervious (some external to ARC) truck trip modeling efforts.
•    Outputs: Truck to O/D matrices and network link specific truck trip counts by truck class and PCEs.

User Manual/User Interface: 

ARC (2010) Travel Forecasting Model Set For the 20 County Atlanta Region. Users Guide. Atlanta Regional Commission. January 2010.