Resumen
This paper used an implementation of the land-use model SILO in Austin, Texas, over a 27-year period with an aim to understand the impacts of the full adoption of self-driving vehicles on the region?s residential land use. SILO was integrated with MATSim for the Austin region. Land-use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or ?autonomous? vehicles (AVs) over the model timeframe versus a scenario in which households? value of travel time savings (VTTS) was reduced by 50% to reflect the travel-burden reductions of no longer having to drive. A third scenario was also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO) and/or higher roadway capacities due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggested an 8.1% increase in average work-trip times when VTTS fell by 50% and VO remained unaffected (the 100% AV scenario) and a 33.3% increase in the number of households with ?extreme work-trips? (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO was raised to 2.0 and VTTS fell instead by 25% (the ?Hi-DRS? SAV scenario), average work-trip times increased by 3.5% and the number of households with ?extreme work-trips? increased by 16.4% in the final model year (versus BAU of 0% AVs). The model also predicted 5.3% fewer households and 19.1% more available, developable land in the city of Austin in the 100% AV scenario in the final model year relative to the BAU scenario?s final year, with 5.6% more households and 10.2% less developable land outside the city. In addition, the model results predicted 5.6% fewer households and 62.9% more available developable land in the city of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario?s final year, with 6.2% more households and 9.9% less developable land outside the city.