User’s Guide for SAM AV Module: Guide for the Texas Statewide Analysis Model with Autonomous Vehicles, Shared-Autonomous Vehicles & Autonomous Trucks

Kockelman, Kara; Vellimana, Maithreyi; Paithankar, Priyanka · 2023 · ROSA P / Texas. Department of Transportation

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Summary

This document serves as a user guide for the SAM AV Module, a modification of the Texas Statewide Analysis Model (SAM-V4) designed to assess the impact of autonomous vehicles (AVs), shared-autonomous vehicles (SAVs), and autonomous trucks (ATrucks) on travel patterns in Texas by the year 2040. Sponsored by the Texas Department of Transportation and developed by researchers at the University of Texas at Austin, the module integrates these emerging modes into the existing TransCAD-based travel demand model to evaluate their effects on freight trips and long-distance passenger travel. The methodology involves significant modifications to the SAM-V4’s Geographic Information System Developer’s Kit (GISDK) scripts and input files. For the passenger model, trip production rates were uniformly increased by 15% to account for induced demand from elderly, unlicensed, and mobility-impaired populations. Short-distance mode choice (<50 miles) was updated using assumed market splits: in areas with or without transit, drive-alone and shared-ride modes were distributed as 40% human-driven vehicles (HVs), 40% AVs, and 20% SAVs, with a 50% reduction in "Other" mode shares shifting to SAVs. For long-distance trips (>50 miles), the nested logit model was expanded to include HVs, AVs, and SAVs nested under party-size categories. Parameters were calibrated based on prior studies, including a 20% reduction in in-vehicle time coefficients for AVs and SAVs to reflect reduced driving burden. For freight, the model introduced ATrucks as a distinct mode nested under the broader truck category with a nesting coefficient of 0.7. ATrucks were assigned operating costs 1.5 times higher than human-driven trucks to account for automation equipment, but travel times were reduced to 42% of human-driven truck times to reflect 24-hour operation capabilities. The traffic assignment component adjusted auto occupancy rates for SAVs by reducing them by 20% to account for empty vehicle miles traveled during repositioning or between passengers. The significance of this work lies in providing a structured, reproducible framework for transportation planners to simulate the future impacts of autonomous technologies on statewide travel demand. By detailing specific parameter adjustments—such as cost rates, value of time, and occupancy factors—the guide enables the evaluation of how AVs, SAVs, and ATrucks may alter Vehicle Miles Traveled (VMT), mode shares, and network congestion in Texas, supporting infrastructure planning and policy development for the 2040 horizon.

Key finding

The SAM AV Module integrates autonomous vehicles and trucks into the Texas statewide travel demand model by modifying mode choice parameters and trip production rates to simulate 2040 travel scenarios.

Methodology

modeling

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• Theoretical Contribution: computational model