Estimating potential increases in travel with autonomous vehicles for the non-driving,elderly and people with travel-restrictive medical conditions |
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| Institution: | 1. Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;2. Engineering and Public Policy, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;3. Civil and Agricultural Engineering Department, Universidad Nacional de Colombia, Carrera 30 #45-03 Ed 214 Of 416, Bogota, Colombia;1. The University of Texas at Austin, 6.9E Cockrell Jr. Hall, Austin, TX 78712, United States;2. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, United States;1. Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall A7A, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;2. Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 123J, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;3. Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 103. 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;1. Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW 2052, Australia;2. Institute for Choice, University of South Australia, 140 Arthur Street, North Sydney, NSW 2060, Australia;1. George Mason University, United States;2. University of Alabama, Birmingham, United States |
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| Abstract: | Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios. |
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| Keywords: | Automated vehicles Elderly travel patterns Vehicle miles traveled 2009 NHTS |
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