  The adoption of transportation policies that prioritized highway expansion
over public transportation has disproportionately impacted minorities and
low-income people by restricting their access to social and economic
opportunities and thus resulting in residential segregation. Policymakers,
transportation researchers, planners, and practitioners have started
acknowledging the need to build a diverse, equitable, inclusive, and accessible
(DEIA) transportation system. Traditionally, this has been done through
survey-based approaches that are time-consuming and expensive. While there is
recent attention on leveraging social media data in transportation, the
literature is inconclusive regarding the use of social media data as a viable
alternative to traditional sources to identify the latent DEIA indicators based
on public reactions and perspectives on social media. This study utilized
large-scale Twitter data covering eight counties around the New York City (NYC)
area during the initial phase of the Covid-19 lockdown to address this research
gap. Natural language processing techniques were used to identify
transportation-related major DEIA issues for residents living around NYC by
analyzing their relevant tweet conversations. The study revealed that citizens,
who had negative sentiments toward the DEIA of their local transportation
system, broadly discussed racism, income, unemployment, gender, ride
dependency, transportation modes, and dependent groups. Analyzing the
socio-demographic information based on census tracts, the study also observed
that areas with a higher percentage of low-income, female, Hispanic, and Latino
populations share more concerns about transportation DEIA on Twitter.
