SERPM 8 includes several components that are not part of the activity based demand modeling process. These represent special travel markets where demand is not easily estimated by the activity based process, including airport, cruiseport, and external travel. The demand estimation for these travel markets is instead generated following the aggregate four-step trip-based approach, which is:
This page provides an overview of the model components that simulate demand in an aggregate manner. For more information on the inputs and parameters of the special generator, truck, and external components , please see the SpecTrkExt Inputs page.
Trips to the airport for purpose of traveling by air, as opposed to working at the airport, are produced through an aggregate special generator at the three international airports in the SERPM area:
The trip production equations apply a simple vehicle trip rate to the number of daily enplanements expected for each alternative at each of the airports. Airport trips are attracted to business, residences, and hotels (total employment, households, and hotel/motels). Airport attraction rates vary by county and area type. Airport productions and attractions are distributed using a destination choice model using network drive-alone time as the impedance, the size term is airport attractions, and it creates a daily P-A airport auto vehicle trip table. These trips are then segmented into assignment time periods using an exogenous set of factors.
Similar to the air passenger trips, surface trips by cruise passengers are generated through a special generator at the three major cruise ports in the SERPM area:
The trip production, attraction, and distribution follow a similar approach as the air passenger trips. A vehicle trip rate is applied to the number of daily cruise passengers for each cruise port. Cruise port trips are attracted to business, residences, and hotels (total employment, households, and hotel/motels). Cruise port attraction rates vary by county and area type. Cruise port productions and attractions are distributed using a destination choice model using network drive-alone time as the impedance, the size term is cruise port attractions, and it creates a daily P-A cruise port auto vehicle trip table. These trips are then segmented into assignment time periods using an exogenous set of factors.
External-external (EE) trips are generated using external station origin and destination targets, or marginal, and a seed trip table with flows between all external stations. A Fratar process is used to adjust the seed trip table such that it matches the origin and destination targets. The daily trip table is then segmented into auto and truck (four-tire, single unit, combination) tables and times of day using factors defined exogenously to the model.
Internal-external (IE) trips are generated using an input of the number of EI and IE trips at each external station. The trip distribution to internal TAZs is simulated using network drive-alone time as the impedance and the size term is internal attractions. The daily P-A auto vehicle trip table is then split into time periods and converted to O-D format using fixed factors.
The SERPM 8 truck model produces heavy truck trips using a matrix-estimated truck trip table based on the existing heavy truck counts and combines that with the estimated change in truck trips as derived through an approach similar to that defined by the Quick Response Freight Manual. Four-tire truck trips are produced through a direct process, no matrix estimation, because they are not separated in the roadway count classifications.
Truck trip ends are generated at each TAZ based on zonal attributes (households, industrial employment, commercial employment, and service employment). Trip rates are segmented by truck class: four-tire, single units with more than four tires, and combinations. Trips are distributed by a destination choice model using the drive-alone travel time as the model impedance. The change in heavy truck trips is then applied to the matrix-estimated heavy truck trip table. Finally, the trips are segmented by time period using exogenously defined factors.