Browsing by Subject "Austin, Texas"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Anticipating land-use impacts of self-driving vehicles in the Austin, Texas, region
    (Journal of Transport and Land Use, 2020) Wellik, Tyler; Kockelman, Kara
    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.
  • Thumbnail Image
    Item
    Examining interaction effects among land-use policies to reduce household vehicle travel: An exploratory analysis
    (Journal of Transport and Land Use, 2019) Choi, Kwangyul; Paterson, Robert
    Numerous studies have suggested that land-use policies can reduce vehicle travel through mode shifting and reduced trip lengths and generation of fewer or more efficient trips. The findings from previous studies also suggest that the combined effect of two or more land-use policies can be significant, although the effects of individual policies appear to be modest. These studies present area-wide impacts of land-use policies on travel and suggest that their effects are additive. However, very little is known about how each land-use policy interacts with the others at different levels of development intensity to reduce vehicle travel. In this study, we explore how three well-known land-use strategies (densification, mixed-use development, and street network improvement) interact with each other by testing possible combinations of land-use factors and focus on how these interactive effects vary by the level of development intensity. Employing ordinary least squares regression analysis using a dataset created for the Austin metropolitan statistical area (MSA) (using 2006 Austin Travel Survey data), we examine the impact of land use on household vehicle travel. Our findings suggest that interaction effects occur, but they vary by development intensity. The results of this study show the importance of considering both threshold (development intensity) and interaction (combination of policies) effects in understanding how land-use factors do and do not affect travel (based on their interactive opposed to only their direct and additive effects). Though this paper uses data from just one MSA and thus is merely suggestive, it does point to a possibly more nuanced use of the commonly prescribed planning and design policy variable to account for variation in effectiveness based on differences in development intensity. For example, we find that greater land-use intensification has higher efficacy in changing vehicle travel behavior in areas with relatively higher development intensity. Future research should include data from a broader array of metropolitan areas and incorporate additional predictor variables that were unavailable for this analysis.