In This Issue
Spring Bridge on Technologies for Aging
March 15, 2019 Volume 49 Issue 1
With the emergence of technologies that can facilitate both independence and quality of life, the subject of this issue is compelling and extremely relevant. The articles explore a variety of aspects of the topic: applications of the Internet of Things, evolving transportation needs, the benefits of social robots, the use of “small data” to enhance understanding and treatment of age-related conditions, a systems approach to assistive technologies, and a framework to help plan for the eventualities of aging.

Planning, Designing, and Engineering Tomorrow’s User-Centered, Age-Ready Transportation System

Friday, March 15, 2019

Author: Joseph F. Coughlin and Samantha Brady

The United States is in the midst of a major demographic transformation toward older citizens. The percentage of the US population aged 65 and -older was 13 percent in 2010, and the Census Bureau projects that by 2020 that number will be 16.1 percent and by 2030 nearly 20 percent (Werner 2011).

The increase in the number of older adults will increase demands on today’s transportation system. Creative development and deployment of new technologies and approaches are required to ensure that people can remain mobile, safe, engaged, and productive throughout their lives. Applying a user-centered approach to develop and integrate information and communications technologies, robotics, and related artificial intelligence applications into tomorrow’s transportation vehicles and infrastructure will be critical to meet the evolving demands of an aging society.

Living Longer, Better in Today’s Longevity Economy

Every 8 seconds in the United States, another baby boomer (of the generation born between 1946 and 1964) turns 73 years old (Heimlich 2010). This historically large postwar cohort of nearly 77 million Americans reshaped the nation’s housing, education, healthcare, and transportation systems. Now in older age, they are placing new demands on transportation that are quantitatively and qualitatively different from those of previous generations of older adults.

The number of aging baby boomers alone doesn’t explain the mobility demands the transportation system will have to address in the coming decades. Previous work by Wachs (1979) identifies education, income, and health as major drivers of mobility. We expand on Wachs’ framework to include the reality of work in “retirement” as well as gender and living arrangements.


Educational attainment is a major factor in an individual’s capacity and desire to travel (Wachs 1979, p. 12). Education often enhances an individual’s efficacy as well as knowledge and curiosity about the world; a better-educated older population is more likely to use transportation not only to satisfy basic needs but also to seek new experiences.

In the United States, educational attainment has risen significantly among older adults. From 1993 to 2015 the percentage of adults aged 65 and older with a high school diploma rose from 60 percent to over 84 percent. Those with a college education more than doubled, from under 12 percent with at least a bachelor’s degree to over 27 percent (Hobbs and Damon 1996; US Census Bureau 2016).


Today’s older adults are far more likely than their forebears to have discretionary income. Americans aged 50 and older control nearly 83 percent of the nation’s household wealth (Coughlin 2017, p. 8), spending power that is likely to translate into more purchases of products, services, and experiences. Transportation is a vital means for these more affluent older consumers to pursue an active lifestyle.


Although approximately 80 percent of adults over the age of 65 manage more than one chronic condition, medical advances have provided innovative strategies to manage diseases (Gerteis et al. 2014). Some diseases that were once considered a death sentence (e.g., some cancers, heart disease) can now be managed for years, enabling many older adults with these conditions to more fully participate in activities outside the home, thereby increasing demand for transportation.


Retirement typically results in fewer vehicle miles -traveled, since the commute to work is no longer part of an individual’s daily transportation. But data show that about 25 percent of men and 16 percent of women remain in the workforce past the traditional retirement age of 65 (BLS 2017, table 3.3). Furthermore, the Bureau of Labor Statistics projects that the labor force participation rate will grow fastest among those over age 65: by 2024 they are poised to account for approxi-mately 8 percent of the total labor force, up from 6 percent in 2018, with an annual growth rate of 4.5–6.4 percent (BLS 2019, table 18b; Toossi and Torpey 2017). They will continue to need transportation to work.


Given their higher levels of workforce participation, education, and income, older women are likely to travel more than their mothers did in their old age. One indicator of this trend is licensure rates: for women over age 65 they rose from around 63 percent in 1994 to about 79 percent in 2016 (Byerly and Deardorff 1995; FHWA 2018a, table DL-220; US Census Bureau 2017). There has also been an increase in average annual miles traveled for these women, from 4,785 miles per driver in 1995 to 6,090 in 2017 (FHWA 2017, 2018b).

Living Arrangements

Where older adults prefer to dwell as they age suggests that there will be greater demand for transportation not only to satisfy travel interests but to meet basic needs. Over 70 percent of today’s 65-plus population lives in suburban or rural locations where public transportation is absent or provides less than optimal service (Administration on Aging 2015). AARP’s Home and Community Preferences survey reports that nearly 60 percent of adults 50 and older say they are likely to remain in their current dwelling as they age (Binette and Vasgold 2018).

Moreover, living alone in older age is becoming the new normal. Solo living, particularly among women over age 75 (47 percent of whom live alone), may require older adults to independently meet their transportation needs without relying on a family member or neighbor (Administration on Aging 2011).

How Older Adults Move Today

These trends in education, income, health, employment, and living arrangements among older adults all suggest that this population will produce unprecedented demand on the transportation system, requiring innovative perspectives and solutions.

Prevalence of the Personal Automobile

Driving is likely to remain the preferred travel mode for aging baby boomers (Coughlin 2001). In 2009 nearly 90 percent of all trips taken by adults over age 65 were in private vehicles; only a very small fraction (2.2 percent) involved public transportation (Lynott and Figueiredo 2011). Table 1 shows that while average annual miles reported by licensed drivers in 2001–2009 declined for individuals under age 65, the number rose more than 7 percent for drivers over age 65. More recently (2009–2017), average annual miles decreased for drivers of all ages—but the decline was lowest among older drivers.

Table 1 

Self-Regulation and Driving Cessation

Some older adults will face diminished capacity and health conditions that will preclude driving as a comfortable or safe mode of transportation. Many of them (especially women; D’Ambrosio et al. 2008) will choose to limit their driving in certain conditions, such as at night, in poor weather, or during peak traffic congestion. For some, self-regulation can be an effective safety strategy; however, it may also represent trips denied and activities forgone, which affect quality of life.

While individuals with mild cognitive impairment or dementia may still be able to drive safely with some regulation, their driving behaviors become increasingly unsafe as these conditions progress (Vaughan et al. 2015; Wadley et al. 2009). For these individuals, driving cessation may become nonnegotiable. Alzheimer’s and other neurodegenerative diseases are forecasted to affect nearly half of the future 85-plus population, and by 2050 driving may not be a transportation option for an estimated 7 million Americans because of -dementia (Alzheimer’s Association 2018). Advanced chronic conditions that impair vision or motor function or cause pain can also make driving impossible.

The Costs of Lost Mobility

Reduced mobility is about more than lost convenience or trips not taken. Mobility has a profound impact on physical and emotional health. A national survey of adults aged 50 and older showed that driving was not readily equated with transportation or access to necessary activities and services; rather, it was perceived as a key to personal independence and freedom (Donorfio et al. 2009). For older adults, reduced driving and driving cessation have been linked with higher risk of depression, cardiovascular disease, stress, poor adherence to healthy behaviors, and a higher likelihood of entry into long-term care (Freeman et al. 2006; Marottoli et al. 1997, 2000; Ragland et al. 2005).

Lack of seamless transportation options reduces trips that make life joyful (e.g., a spontaneous outing for ice cream on a hot summer evening) and also limits chance encounters with friends. Easy and regular access to friends and places that enhance social interaction is key to well-being. Social isolation is directly related to poorer health, especially for older adults (Cacioppo and Hawkley 2003; Cornwell and Waite 2009). One study equated social isolation with the mortality risk of smoking 15 cigarettes per day (Holt-Lunstad et al. 2015).

User-Centered Continuum of Micro- and Macro-Mobility Patterns

Some analysts suggest that transportation policy should shift from a focus on engineering for the optimal movement of people and goods to a more holistic perspective on both small trips and large-scale operations—that is, transportation as a contributor to the well-being of individuals with varying desires and needs (Marshall 2001; Wheeler 2004, p. 76). Such an approach enables transportation planners and engineers to better understand how to most effectively design and operate a transportation system that meets a range of needs while considering multimodal alternatives.

A user-centered framework with a continuum of transportation activities from micro- to macro-mobility needs is introduced in figure 1. Micro-mobility refers to travel around the home and in the immediate community; macro-mobility entails trips into and beyond the traveler’s broader home region.

Figure 1 

Figure 1 depicts five broad trip types. Errands such as food shopping and medical appointments are most likely micro-mobility trips in the immediate community or nearby region. Social trips may extend beyond the community for visits with family and friends and to participate in faith or other activities. Work-related trips may be a macro-mobility demand. Lifestyle travel for recreation, visits with extended family, and vacations is likely to cross regions and involve multiple modes of travel.

Micro-Mobility Modes of Transportation

The capacity to move freely around the neighborhood may be supported by new technologies and modes of transport.

“Cobotics” (robots that have been designed and built to physically collaborate with humans; Colgate et al. 1996) and exoskeletal systems used by assembly line and logistics workers to extend strength and reduce fatigue may help older adults as well. Panasonic (2018) has developed exoskeletons to assist not only -employees in jobs with high physical strain but also caregivers and older adults. The company’s newest exoskeleton is designed to be put on and taken off without assistance, a vital feature for older adults living alone. Exoskeletal systems could help elderly individuals remain in their home by affording them improved mobility for routine in-home tasks and travel in the neighborhood.

For very short trips, small-vehicle services may enable micro-mobility. For example, in retirement communities such as The Villages in central Florida, residents travel primarily by golf cart (US Census Bureau 2016).

Modes Spanning Micro- and Macro-Mobility

Public Transit

Public transportation can be an option for both micro- and macro-mobility needs. Unfortunately, insufficient system availability and bounded budgetary resources limit the capacity of bus and rail services and demand response systems (which respond to individual user demand and do not maintain fixed schedules or routes) to meet the needs of older adults. Moreover, despite the Americans with Disabilities Act, many older adults still find that physical access to buses and rail services is difficult.

Diminished vision and even minor cognitive impairment may hinder wayfinding in transit hubs and other public spaces. New tools are needed to help transportation engineers, planners, and developers understand the mobility challenges of older users. One such tool is the Age Gain Now Empathy System (AGNES), a suit developed by the Massachusetts Institute of Technology AgeLab that simulates some of the physical changes that accompany age and the onset of various chronic conditions (figure 2; Lavallière et al. 2017). The wearer experiences age-related physical limitations when navigating elements of the built environment such as streets and sidewalks, steps, stores, and public transportation.

Figure 2 

Novel systems are under development to improve access to and availability of public transit resources. On-demand app-based bus services, often called microtransit, could help link users with public transit services (Westervelt et al. 2018). Supporting these efforts, Carnegie Mellon researchers have developed a smartphone app called Tiramisu that improves the visibility of transit and microtransit options (Steinfeld et al. 2011). Mobility-by-app services such as Uber and Lyft provide transportation options with wheelchair accessibility, making them effectively a private transit fleet for people of all ages.

New Features in the Personal Automobile

As discussed above, the automobile remains the dominant mode of transportation for older Americans, so technologies that facilitate driving are of particular value to them. Advanced Driver Assistance Systems improve both driver performance and safety, supporting operator performance and, for older adults in particular, compensating for diminished vision, delayed reaction time, and even reduced neck and back rotation due to poor flexibility or pain (Reimer et al. 2008). Such systems include lane departure warnings, pedestrian and side impact detection, adaptive cruise control, collision warning systems, heads-up displays, and advanced navigation.

Figure 3 

The average age of a new car buyer in the United States is about 52 years old (Kurz et al. 2016). Luxury vehicle buyers are typically older than that, making the mature driver the lead adopter of many of the automobile industry’s newest systems. An older driver of a new car must reconcile decades of driving experience with unfamiliar automotive technologies and interfaces (figure 3), yet there has been little focus on educating new car buyers about effectively and safely using new driver assistance systems. One study found wide variation among brand dealerships in explaining new vehicle technologies to prospective vehicle buyers (Abraham et al. 2017). A fundamental engineering requirement for new automotive systems is a simple and intuitive learning process, lest driving itself become the new driver distraction safety problem (Koo et al. 2014; Reimer 2014; Souders and Charness 2016).

Autonomous Vehicles

The “Holy Grail” of vehicle technology is the fully autonomous vehicle, heralded as a comprehensive mobility solution for older adults of all levels of capacity. But user-centered engineering challenges will still need to be solved to ensure complete and seamless mobility for older travelers.

A user-centered approach requires engineering the entire trip. Curb-to-curb vehicle travel research is a well-established field, but what about the first and last 25–50 feet of movement outside the car? A person who is physically or cognitively unable to drive is likely to require assistance from her home and into the vehicle and then out of the vehicle to her destination—and then back again for the return trip. In today’s transportation landscape, a family member, friend, or trained driver typically solves this challenge. But if tomorrow’s driverless vehicles are to be a seamless solution for older adult mobility, engineers must solve for the traveler’s complete trip, and not merely that which involves the vehicle itself.

Macro-Mobility Modes of Transportation

Older adults are already a core market for the leisure travel industry, and the next generation of older adults is likely to travel even more for leisure, experience, and family visits. Such macro-mobility needs often entail air travel, which presents frictions and frustrations for even the most experienced traveler.

Challenges for older adults who travel by plane include wayfinding in the unfamiliar, complex, and harrying environment of the airport; reading signage or operating automatic ticketing services that can be difficult to read or unclear in meaning; clearly hearing critical terminal announcements over the intercom; and getting their luggage from the curbside through check-in, security, and baggage claim (TRB 2014).

New airport technologies and systems exist or are being developed to ease these challenges. For example, if transitions onto and off moving walkways are engineered effectively, they can reduce older traveler fatigue. Boeing engineers and designers have used AGNES-type suits to understand the difficulty experienced by older travelers when boarding a plane and stowing luggage, leading to a rethinking of the engineering of luggage racks, seating, and lighting (Spicer 2005). Robotic airport assistants are under development to carry baggage for overburdened travelers (Wu 2018).

Almost Like Being There: Technology and Mobility Substitution

A user-centered approach to transportation planning and engineering goes beyond defining mobility as simply travel from point A to point B: it is about connecting to services, activities, and people. Many older adults are not able to access transportation because of their location, poor health, or disability. Fortunately, rapidly developing services and technologies may make even the most isolated feel connected.

The Sharing Economy

The sharing economy is defined as “an economic system in which assets or services are shared between private individuals, either for free or for a fee, typically by means of the internet.”[1] Common examples include Lyft, Uber, Taskrabbit, and Instacart. Although it appears to be designed for the lifestyles of younger people and urban professionals, the sharing economy and e-commerce services (e.g., banking, online shopping) could also form the foundations of virtual assisted living for some older adults. Meals can be delivered to one’s door with a smartphone app. Basic shopping needs can be satisfied online and, in many areas, delivered to the doorstep within hours. A recent study showed that accessing services through the sharing economy may be not only effective in satisfying an older adult’s needs but also more affordable than moving to senior housing (Miller et al. 2018).

Healthcare Technology

Health care is moving increasingly into the home. Telemedicine and mobile technology enable caregivers and families to virtually monitor the health and well-being of loved ones living alone. Services provide online medical consultations or therapy sessions (e.g., Teledoc, Dr. on Demand). Others offer home care on demand (e.g., Honor, DispatchHealth), enabling older adults and families to hire a home aide through a smartphone application.

Virtual Travel

Satisfying needs is critical, but connecting to friends and fun is also vital to quality aging. Social media and richer technological experiences are enabling older adults to connect with family and friends with user-friendly apps and interfaces on computers, tablets, and smartphones. For those with reduced mobility, virtual reality can even “bring” them to distant destinations on their bucket list, like Paris or Machu Picchu.

Planning and Engineering Tomorrow’s Age-Ready Transportation System

The transportation needs of an aging nation demand the creative application of engineering and design to ensure that the transportation system is both age-friendly and age-ready: ready for a new generation of older adults who are likely to be more engaged in lifelong activities than previous cohorts. At the same time, older adults remain a highly heterogeneous population, with significant variation in age, socioeconomic status, education levels, health, and technology access and experience. Designers and policymakers aiming to create and implement technology to enhance mobility for older adults must carefully consider how to best serve this diverse generation’s mobility needs, ideally based on input from the users themselves.

The creative application of technology to seamlessly respond to the interests and needs of this new generation of travelers requires not only the development of new vehicles and infrastructure, but a user-centered approach to build a system that enables everyone to live both longer and better.


The authors thank the MIT Center for Transportation & Logistics, AARP, Tivity Health, Toyota, the Hartford Financial Services Company, and the US Department of Transportation University Transportation Centers Program for supporting research on which this paper is based, and the MIT AgeLab’s Adam Felts and Carly Dickson and The Bridge’s Cameron Fletcher for keen editorial eyes and graphics.


AARP. 2010. Approaching 65: A Survey of Baby Boomers Turning 65 Years Old. Washington.

Abraham H, McAnulty H, Mehler B, Reimer B. 2017. Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems. Transportation Research Record 2660:7–14.

Administration on Aging. 2011. A Profile of Older Americans: 2011. Washington: US Department of Health and Human Services.

Administration on Aging. 2015. A Profile of Older Americans: 2015. Washington: US Department of Health and Human Services.

Alzheimer’s Association. 2018. Alzheimer’s Disease Facts and Figures. Alzheimer’s & Dementia 14(3):367–429.

Binette J, Vasold K. 2018. 2018 Home and Community -Preferences: A National Survey of Adults Age 18-Plus. Washington: AARP Research.

BLS [Bureau of Labor Statistics]. 2017. Employment Projections. Washington.

BLS. 2019. Labor Force Statistics from the Current Population Survey. Washington.

Byerly E, Deardorff K. 1995. Current Population Reports: National and State Population Estimates: 1990 to 1994 (P25-1127). Washington: US Census Bureau.

Cacioppo JT, Hawkley LC. 2003. Social isolation and health, with an emphasis on underlying mechanisms. Perspectives in Biology and Medicine 46(3):S39–S52.

Colgate JE, Wannasuphoprasit W, Peshkin MA. 1996. Cobots: Robots for collaboration with human operators. Proceedings of the International Mechanical Engineering Congress and Exposition, Nov 17–22, Atlanta.

Cornwell EY, Waite LJ. 2009. Social disconnectedness, perceived isolation, and health among older adults. Journal of Health and Social Behavior 50(1):31–48.

Coughlin J. 2001. Transportation and Older Persons: Perceptions and Preferences. Washington: AARP Public Policy Institute.

Coughlin J. 2017. The Longevity Economy. New York: -PublicAffairs.

D’Ambrosio LA, Donorfio LK, Coughlin JF, Mohyde M, -Meyer J. 2008. Gender differences in self-regulation patterns and attitudes toward driving among older adults. -Journal of Women and Aging 20(3-4):265–282.

Donorfio LK, D’Ambrosio LA, Coughlin JF, Mohyde M. 2009. To drive or not to drive, that isn’t the question: The meaning of self-regulation among older drivers. Journal of Safety Research 40(3):221–226.

FHWA [Federal Highway Administration]. 2017. National Household Travel Survey: Average annual vehicle miles of travel per driver, 2017 (by sex and age). Online at

FHWA. 2018a. Highway Statistics 2016. Washington.

FHWA. 2018b. 1995 Nationwide Personal Transportation Survey. In: Our Nation’s Highways: Selected Facts and Figures 2000. Washington: FHWA Office of Highway Policy Information.

Freeman EE, Gange SJ, Muñoz B, West SK. 2006. Driving status and risk of entry into long-term care in older adults. American Journal of Public Health 96(7):1254–1259.

Gerteis J, Izrael D, Deitz D, LeRoy L, Ricciardi R, Miller T, Basu J. 2014. Multiple Chronic Conditions Chartbook. AHRQ Publications No. Q14-0038. Rockville MD: -Agency for Healthcare Research and Quality.

Gurwitz JH. 2004. Polypharmacy: A new paradigm or quality drug therapy in the elderly? Archives of Internal Medicine 164(18):1957–1959.

Heimlich R. 2010. Baby Boomers Retire. Washington: Pew Research Center.

Hobbs F, Damon B. 1996. Current Population Reports, -Special Studies, P23-190, 65+ in the United States. Washington: US Census Bureau.

Holt-Lunstad J, Smith TB, Baker M, Harris T, -Stephenson D. 2015. Loneliness and social isolation as risk factors for mortality. Perspectives on Psychological Science 10:227–237.

Koo J, Kwac J, Ju W, Steinert M, Leifer L, Nass C. 2014. Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing 9(4):269–275.

Kurz C, Feng D, Vine D. 2016. The young and the carless? The demographics of new vehicle purchases. Washington: Board of Governors of the Federal Reserve System.

Kurz D, Fedosov A, Diewald S, Guttier J, Geilhof B, -Heuberger M. 2014. Towards mobile augmented reality for the elderly. Poster at IEEE International Symposium on Mixed and Augmented Reality, Sep 10–12, Munich. doi:10.1109/ismar.2014.6948447.

Lavallière M, D’Ambrosio L, Gennis A, Burstein A, Godfrey KM, Waerstad H, Puleo RM, Lauenroth A, Coughlin JF. 2017. Walking a mile in another’s shoes: The impact of wearing an age suit. Gerontology and Geriatrics Education 38(2):171–187.

Lococo K, Staplin LK. 2006. Literature review of polypharmacy and older drivers: Identifying strategies to study drug usage and driving functioning among older drivers. Washington: US Department of Transportation, National Highway Traffic Safety Administration.

Lynott J, Figueiredo C. 2011. How the Travel Patterns of Older Adults Are Changing: Highlights from the 2009 National Household Travel Survey. Washington: AARP Public Policy Institute.

Marottoli RA, Mendes de Leon CF, Glass TA, Williams CS, Cooney LM Jr, Berkman LF. 1997. Driving cessation and increased depressive symptoms: Prospective evidence from the New Haven EPESE. Journal of the American Geriatrics Society 45:202–206.

Marottoli RA, Mendes de Leon CF, Glass TA, Williams CS, Cooney LM Jr, Berkman LF. 2000. Consequences of driving cessation: Decreased out-of-home activity levels. Journal of Gerontology: Social Sciences 55B:S334–S340.

Marshall S. 2001. The challenge of sustainable transportation. In: Planning for a Sustainable Future, eds Batty S, Davoudi S, Layard A. London: Routledge.

McGuckin N, Fucci A. 2018. Summary of Travel Trends: 2017 National Household Travel Survey FHWA-PL-18-019. Washington: US Department of Transportation, Federal Highway Administration.

Miller J, Ward C, Lee C, D’Ambrosio LA, Coughlin JF. 2018. Sharing is caring: The potential of the sharing economy to support aging in place. Gerontology and Geriatrics -Education.

Montella C, Perkins T, Spletzer J, Sands M. 2014. To the bookstore! Autonomous wheelchair navigation in an urban environment. In: Field and Service Robotics: Results of the 8th International Conference, eds Yoshida K, Tadokoro S. pp 249–263. Berlin: Springer.

Panasonic. 2018. ATOUN to Assist Everyday Lives of the Elderly with the Wearable Robot, “Powered Wear.” Online at

Ragland DR, Satariano WA, MacLeod KE. 2005. Driving cessation and increased depressive symptoms. Journal of -Gerontology: Medical Sciences 60A:399–403.

Reimer B. 2014. Driver assistance systems and the transition to automated vehicles: A path to increase older adult safety and mobility? Public Policy and Aging Report 24(1):27–31.

Reimer B, D’Ambrosio LA, Coughlin JF, Puleo R, Cichon J, Griffith JD. 2008. The effects of age on spinal rotation during a driving task. Transportation Research Record 2078:57–61.

Ryan CL, Bauman K. 2016. Educational Attainment in the United States: 2015–2050. Washington: US Census Bureau.

Souders D, Charness N. 2016. Challenges of older drivers’ adoption of advanced driver assistance systems and autonomous vehicles. In: Human Aspects of IT for the Aged Population. Healthy and Active Aging Lecture Notes in Computer Science, eds Zhou J, Salvendy G. pp 428–440. Basel: Springer International Publishing.

Spicer K. 2005. Engineers, designers walk in the shoes of older passengers to understand needs of future air travelers. -Boeing Frontiers 4(8).

Steinfeld A, Zimmerman J, Tomasic A, Yoo D, Aziz RD. 2011. Mobile transit information from universal design and crowdsourcing. Transportation Research Record 2217(1):95–102.

Toossi M, Torpey E. 2017. Older workers: Labor force trends and career options. Washington: Bureau of Labor Statistics, US Department of Labor.

TRB [Transportation Research Board]. 2014. Impacts of Aging Travelers on Airports. ACRP Synthesis Report #51. Washington: National Academies Press.

US Census Bureau. 2016. ACS demographic and housing estimates, 2012-2016. American Community Survey 5-year estimates (DP05). Washington.

US Census Bureau. 2017. Annual estimates of the resident population by single year of age and sex for the United States, April 1, 2010 to July 1, 2017 (NC-EST2017-AGESEX-RES). Washington.

Vaughan L, Hogan PE, Rapp SR, Dugan E, Marottoli RA, Snively BM, Shumaker SA, Sink KM. 2015. Driving with mild cognitive impairment or dementia: Cognitive test performance and proxy report of daily life function in older women. Journal of the American Geriatric Society 63(9):1774–1782.

Wachs M. 1979. Transportation for the Elderly: Changing Lifestyles, Changing Needs. Oakland: University of -California Press.

Wadley VG, Okonkwo O, Crowe M, Vance DE, Elgin JM, Ball KK, Owsley C. 2009. Mild cognitive impairment and everyday function: An investigation of driving performance. Journal of Geriatric Psychiatry and Neurology 22(2):87–94.

Werner C. 2011. The older population: 2010. 2010 Census Briefs, C2010BR-09. Washington: US Census Bureau.

Westervelt M, Huang E, Schank J, Borgman N, Fuhrer T, Peppard C, Narula-Woods R. 2018. UpRouted: Exploring microtransit in the United States. Washington: Eno Center for Transportation.

Wheeler SM. 2004. Planning for Sustainability: Creating Livable, Equitable and Ecological Communities. Abingdon, UK: Routledge.

Wu L. 2018. Three ways robots could soon be a part of your airport experience. Forbes, July 31.


[1]  Oxford English Dictionary, economy

About the Author:Joseph Coughlin is director of the AgeLab and Samantha Brady a research specialist in the AgeLab at the Massachusetts Institute of Technology Center for Transportation & Logistics.