US walking distances and pedestrian safety outcomes + integrating autonomous vehicles into Texas’ Statewide Analysis Model

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dc.contributor.advisorKockelman, Kara
dc.creatorVellimana, Maithreyi
dc.date.accessioned2024-04-26T20:34:06Z
dc.date.available2024-04-26T20:34:06Z
dc.date.issued2023-08
dc.date.submittedAugust 2023
dc.date.updated2024-04-26T20:34:07Z
dc.description.abstractThis thesis consists of two, very distinct parts. The first part focuses on pedestrian safety, while the second investigates Texas travel impacts of autonomous cars and trucks. Part 1 examines walk distances across the United States by time of day and year, using data from the National Household Travel Survey 2016/2017, with the aim to understand factors contributing to higher pedestrian deaths at night across various states. Using hurdle regression to predict daily walk-miles traveled (WMT) and nighttime WMT across the US, the study finds that the decision to walk and distances walked on each survey day and night vary significantly with demographic attributes (like race, income, worker status and education), time of year, latitude, state of residence, and other factors. Longer daylight hours and more nighttime walking do not appear to be the reasons for some states’ much higher pedestrian fatality rates. Additionally, there is no evidence from traffic fatality rates due to alcohol consumption or overall alcohol consumption per capita to support this result. Differences in built environments, law enforcement, and aggressive driving may be key factors for much higher pedestrian death rates in southern settings. Part 2 focuses on predicting travel patterns for the year 2040 by integrating mode options for autonomous vehicles (AVs), shared autonomous vehicles (SAVs), and automated trucks (ATrucks) into the Texas Statewide Analysis Model (SAM). Initial results suggest that for long-distance passenger trips, human-driven vehicles (HVs) are estimated to remain more popular than AVs and SAVs, with roughly 10% increases (for both business and non-business trips) for the shared ride 3+ (SR3+) mode option on trips below 400 miles (drawing primarily from air and intercity-rail trips). The airline mode remains preferred choice for trips over 400 miles (80% of all >400 mile person-trips and 18% of total PMT, which is 11.6% of all person-trips). Introduction of ATrucks had its biggest impacts on freight movements for the oil, gas, and mining sectors. Major roadways for freight-truck movement (under both the before and after-AVs scenarios) are US 60, US 99, I-40, I-35, US 87, US 287 and US 57. While the model applications provide some valuable insights, SAM model limitations (such as exclusion of bus as a mode in long-distance passenger model, and fixed mode splits for most passenger trips) highlight the need for future research and improvements. Another limitation in this study is that only the mode choice step of a model has been updated to account for introduction of these new modes. So this model will predict the effect on mode splits (and trip distribution when feedback loops are included), but it cannot reflect the change in trips due introduction of these new modes. Future wok will focus on updating trip generation to reflect effect on trip production. Addressing these limitations and calibrating the model mode choice parameters will enhance the study and the scope of the SAM model, enabling a more comprehensive analysis of the impacts of AVs across the nation and Texas.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/2152/124933
dc.identifier.urihttps://doi.org/10.26153/tsw/51535
dc.language.isoen
dc.subjectPedestrian safety
dc.subjectWalk-miles traveled
dc.subjectStatewide Analysis Model
dc.subjectDarkness
dc.subjectAutonomous vehicles
dc.titleUS walking distances and pedestrian safety outcomes + integrating autonomous vehicles into Texas’ Statewide Analysis Model
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.nameMaster of Science in Engineering
thesis.degree.programTransportation Engineering

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