Travel Demand Model - OKI

OKI/MVRPC Travel Demand Model – Version 6.0Modeling the flow of truck trips between various origins and destinations is complicated by therelationships mentioned above, namely supply chains, inter-modal transfers and transshipment facilities.One method of getting at these relationships is to tie regional truck movements to a commodity flowsdatabase and, ultimately, to relate the production and use of commodities by different industries toemployment. Commodity flow data is commonly used for modeling freight movements at the state andnational level. A proprietary database of commodity flows was acquired for this project from a leadingcommercial vendor, Reebie Associates. The data were expressed in terms of annual truck shipments bycommodity type between counties in the study region as well as truck trips passing through the region.Subsequent investigation revealed that important county-to-county movements were missing from thedata and apparently unavailable. Another version of the Reebie Transearch database, expressed inannual tons of commodity shipments, was furnished by the Ohio Department of Transportation (ODOT);however, this too was found to lack some county-to-county flow information and had otherinconsistencies. In general, commodity flow data is not currently developed to account for intra-urbantruck movements, particularly short-haul goods distribution to retail stores and trips by smaller trucks (SUtrucks), such as in package delivery, construction and garbage hauling. These findings led to theelimination of the work program task to develop a commodity flows database.In lieu of modeling truck trips by commodity flow relationships, a “gravity” model, similar to those used inmodeling person trips, can be used to approximate commercial vehicle movements between zones. Thisapproach has been implemented in the truck model developed for the CRM.Lacking commercial vehicle survey data for calibration, the trip generation equations and gravity modelimpedance functions use modified versions of parameters published in the Quick Response FreightManual (USDOT 1996) to produce initial estimates of SU and MU truck trip tables. The truck model isthen calibrated using a synthetic matrix estimation (SME) method. SME uses the initial trip table estimateas a “seed matrix,” which is then adjusted such that assignment of the table to the highway networkresults in truck trip flows that come close to matching observed truck traffic counts, through successiveiterations. SME adjusts not only the flow pattern, but also the number of trips produced, effectivelycalibrating both trip generation and distribution stages simultaneously.Development of the truck model using SME methods required the development of a database of trucktraffic counts for use in calibration and validation. In addition, the mathematics involved in the SMEprocedure necessitated the use of a coarser zone system than that used by the passenger-vehicle model.An auxiliary freight analysis zone (FAZ) system was formed from groups of the transportation analysiszone (TAZ) system of the combined regional model. An auxiliary version of the CRM network wascreated to link to the FAZ system and to use in SME.The truck model was not developed using the Tranplan modeling environment of the passenger model.Instead, the TransCAD transportation modeling program was used, owing largely to its geographicinformation system (GIS) capabilities and built-in synthetic matrix estimation procedure. The daily SUand MU truck trip tables produced in TransCAD are post-processed in a separate program to convert FAZflows to TAZ flows and to allocate these daily trips to the four assignment periods. The resulting triptables then become static inputs in the Tranplan model stream of the CRM and can be used in planninganalysis.The truck model forecasting procedure uses a growth factor matrix adjustment method to generatefuture-year daily truck trip tables based on forecast growth in zonal employment and households. Basedon time series data, the procedure assumes that SU and MU truck trip generation rates per employee willincrease over time because of forecasted improvements in productivity, varying by industry sector. Thisassumption is reflected in the development of productivity deflation factors, which are then applied in thegrowth factor calculations. As with the base-year truck trip tables, the forecast-year truck trip tables areallocated to assignment periods and implemented as static inputs in the Tranplan model stream.Truck Model - Introduction 3