Trucks

The structure of the truck model in SeaCast is largely unchanged from the 4k Trip-Based Model. The truck model defines a truck based on relative weight classes and separates medium & heavy trucks for analysis purposes, which are defined to match the definitions used for collecting truck counts by the WSDOT. While these definitions rely primarily on weight, these categories also are loosely correlated to other defining characteristics of trucks for other purposes. The following general categories of trucks are used:

• Medium trucks are defined as single unit, six or more tires, two to four axles and 16,000 to 52,000 lbs. gross vehicle weight; and
• Heavy trucks are defined as double or triple unit, combinations, five or more axles, and greater than 52,000 lbs. gross vehicle weight.

In these definitions, the medium trucks are directly correlated to single-unit trucks collected in the WSDOT truck counts, and heavy trucks are directly correlated to double- and triple-unit trucks in the counts. Commercial vehicle trips (Light Trucks) are not included in the truck model because they are captured, at least in part, by Daysim (the Activity Based Model engine in SeaCast).

Truck Model Improvements

Although the structure remains similar, many improvements have been made to the forecasting of truck demand in SeaCast. These improvements include:

• Inclusion on hundreds of additional medium and heavy truck counts on both arterials and freeways across the region
• Improved generation of truck trips by providing and crosswalk between the employment that generates truck trips and the underlying land use required for industrial and truck activity
• Improved network attributes including restricting truck activity for parts of the network that do not allow heavy truck traffic.
• Updated truck special generators for the Ports of Seattle, Tacoma and Everett
• Updated external truck inputs provided by the Washington State Department of Transportation

The inclusion on increased truck data was used to further improve the validation of truck activity in the model and weer the first other improvements for trucks planned over the next year.

Employment Data used for Truck Generation

The socioeconomic data used in the truck model are consistent with those data used in the passenger model, except that the employment data are stratified into more employment categories. This process provides more accuracy for truck travel and allows for a direct relationship between the commodities being estimated in the external trip model and the allocation of these commodities to TAZs within the region. The employment categories used in the truck model are:

• Agriculture/Forestry/Fishing
• Mining
• Construction
• Manufacturing (Products and Equipment)
• Transportation/Communication/Utilities
• Wholesale
• Retail
• FIRES
• Education and Government

Truck Trip Rates

Truck trip production rates for internal truck travel were developed separately for the three different truck types: light, medium, and heavy. These are presented in Table 29. Truck trip consumption rates are the equivalent of truck trip attraction rates, and are provided in Table 30 by truck type and industry.

Truck Trip Production Rates

Employment Category	Heavy	Medium
Agriculture/Forestry/Fishing	0.2366	0.0889
Mining	0.3405	0.0889
Construction	0.0856	0.0998
Manufacturing- Products	0.2661	0.0858
Manufacturing- Equipment	0.0953	0.0858
TCU	0.1075	0.2079
Wholesale	0.1337	0.2552
Retail Trade	0.0463	0.1637
FIRES	0.0044	0.0434
Education & Government	0.0297	0
Households	0.0031	0.0358

Truck Trip Attractions Rates

Employment Category	Heavy	Medium
Agriculture/Forestry/Fishing	0.0988	0.2831
Mining	5.0897	14.8073
Construction	0.029	0.0876
Manufacturing- Products/Equipment	0.0208	0.0538
TCU	0.0378	0.0998
Wholesale	0.0087	0.0352
Retail Trade	0.0032	0.0123
FIRES	0.0088	0.0375
Education & Government	0.0073	0.016
Households	0.0071	0.0385

Truck Refactoring

The trip rates for trucks are based on a variety of data sources including both local and national data. Due to the variability of data availability locally and the strong differences between truck travel by region, a set of regional adjustment factors were calculated for truck production and attraction rates. These adjustments were developed iteratively and are based on observed truck counts across the region.

Truck Trip Adjustment Factors

Area	Heavy	Medium
Productions	0.413	0.309
Attractions	1.375	0.5