Generation 6 of the Indiana Statewide Travel Demand Model (ISTDM6) development includes an update to a 2010 base year and calibration and validation of the updated model. This phase also incorporates American Trucking Research Institute (ATRI) data to enhance the truck model and 2009 NHTS data for updating trip generation, distribution, mode share and vehicle occupancy rates. The model is designed to produce more reliable auto and truck forecasts for a variety of planning studies within Indiana including the I-69 corridor studies. This presentation and paper evaluates the nuances associated with the statewide model. The focus is on applying origin destination matrix estimation (ODME) adjustments to modeled trip tables based on observed traffic counts.

The ODME adjustment procedures for auto and truck established in the fifth generation of the model (ISTDM5) are maintained in ISTDM6. However, different approaches to ODME with multi-class demand and assignments are used in ISTDM6 based on newly available data and sensitivity testing of the ODME procedure. This paper evaluates the sensitivity of the ODME procedure in the auto and truck applications on both base year and forecast year models.

For the auto trips, ODME application sensitivities to varying counts, count weights, and bounds on the adjustments are evaluated. Various counts and count weights are tested due to the discrepancy in traffic counts between the ISTDM5 (2006 base year) and ISTDM6 (2010 base year), especially on rural interstates. Various bounds on the ODME adjustment parameters are tested for future year validation.

For the truck trips, ODME application sensitivities to the input seed trips are analyzed. The input seed trip table is based on a large sample of truck GPS data available from ATRI. The question analyzed is: What does the ATRI data represent? Various combinations of modeled freight, single-unit, and multi-unit trips are tested in representing the ATRI data.

This paper concludes with discussion of how the sensitivity analyses are used to determine the most appropriate use of the ODME application that provides higher confidence in the resulting estimated trips and in its application in future year models.