To adequately measure the extent of inequities in current systems and identify interventions to address these inadequacies in transportation, specific, standardized demographic data that adequately accounts for the diversity of the population, including multiple dimensions of disability, must be collected. In 2023, 61 million Americans live with some form of disability (Centers for Disease Control and Prevention, 2023), including visual, hearing, mobility, and cognitive disabilities. Unfortunately, current systems for collecting data on disability are inadequate, perpetuating the existing inequities of transportation systems between those with and without disabilities and limiting the impact that transportation planners can make to reduce them. There are three main limitations in current national and local travel surveys in identifying disability populations and understanding the travel experiences of disabled travelers holistically:

Lack of Measurement Reflecting Diverse Types of Disability. Measures of disability in current surveys primarily focus on travel-limiting medical conditions and do not capture the diversity within the disability community (Levine and Karner, 2022). The exclusion of questions accounting for the diversity within the disability population leads to a critical gap in transportation surveillance, especially for disability subgroups, such as those on the autism spectrum.

Lack of Data on Travel Barriers. The social model of disability frames disability as a condition created within the context of the environment in which a person lives (Shakespeare, 2010). An individual’s disability status is exacerbated or ameliorated by the physical places, policies, and resources available to them. For example, individuals with physical limitations are “disabled” by missing curb ramps rather than by their impairments. Similarly, individuals with hearing limitations are disabled by nonexistent visual notifications of upcoming bus stops. The absence of information about the disabling experience of interactions with such barriers limits our understanding of what shapes the decisions of people with disabilities regarding travel and ultimately where treatments and interventions can be most effective.

Lack of Appropriate Data Collection. Several travel surveys (e.g., NHTS) do not collect data on trips that are not taken but that are desired or needed. Accounting for these trips not taken is particularly salient as numerous research studies have identified how physical, social, and policy transportation barriers limit trip taking among the disabled population (Bezyak, 2017). Current methods in disability and transportation research tend to focus on cross-sectional research design, which limits our understanding of disability and transportation as disability changes over time. Also, data is limited only to the utilization of certain services, such as paratransit, and does not capture the experience of the complete trip, which can be across service modes. Finally, current methods lack guidance on ensuring the inclusion of people with cognitive disabilities in the sample and fully participating in the data collection (Rios, 2016).

There is also a lack of systematized evaluation models that can be used to assess the quality of transportation, particularly regarding its ability to satisfy the needs of citizens with temporary or permanent disabilities. An immersive virtual environment (IVE) tool is proposed to support developing and applying inclusive, safe, resilient, and sustainable accessible qualities onto community public transportation systems. IVEs are computer-based, artificial, digital realms that users can access through specialized equipment. This equipment provides sensory stimuli, creating the illusion of immersion within the digital environment. IVEs would be a valuable evaluation tool for accessible mobility strategies, including in emergencies, allowing relevant stakeholders and end-users to observe and evaluate alternative implementation proposals (Soares, F. et al. 2021). To achieve the targeted transport improvements, the development of quality assessment tools is required to gauge and plan the level of service to be provided. 

References

Centers for Disease Control and Prevention. Disability and Health Data System (DHDS) [Internet]. [updated 2018 May 24; cited 2018 August 27]. Available from: http://dhds.cdc.gov

Shakespeare, Tom. "The Social Model of Disability." The Disability Studies Reader. Ed. Lennard J. Davis. New York: Routledge, 2010. 266-73. Print. (Pre-print copy.)

Bezyak, J. L., Sabella, S. A., & Gattis, R. H. (2017). Public transportation: an investigation of barriers for people with disabilities. Journal of Disability Policy Studies, 28(1), 52-60. https://doi.org/10.1177/1044207317702070

Rios, D., Magasi, S., Novak, C., & Harniss, M. (2016). Conducting Accessible Research: Including People With Disabilities in Public Health, Epidemiological, and Outcomes Studies. American Journal of Public Health, 106(12), 2137-2144. https://doi.org/10.2105/AJPH.2016.303448 

Soares, F., Silva, E., Pereira, F., Silva, C., Sousa, E., & Freitas, E. (2021). To cross or not to cross: Impact of visual and auditory cues on pedestrians’ crossing decision-making. Transportation research part F: traffic psychology and behaviour, 82, 202-220.