Efficacy or Equity? A Public Transit Operations Method for Balancing Costs and Local Need: A COVID-19 Case Study in Austin,TX

Abstract

This study explores the operational dynamics of public transit during the early stages of the COVID-19 pandemic, focusing on the Capital Metro Transit Authority in Austin, TX. The pandemic induced a dual challenge of declining ridership and the urgent need to serve transit-dependent populations, particularly essential workers who are predominantly from lower income and minority backgrounds. Using the analytical hierarchy process (AHP), this research develops a method for transit authorities to balance demand, supply, and equity in real-time transit operations. Daily operational metrics and demographic data spanning from January 2019 to May 2022 within a 0.25-mi radius from transit stops were utilized in the analysis. The study revealed that traditional metrics often overlook the intricate needs of transit-dependent populations. Specifically, the AHP model indicated that certain routes, which had been canceled, should instead have continued at a reduced rate because of their high equitable need. Particularly affected by these operational changes were foreigners and individuals residing more than 5 mi from the central business district, who suffered disproportionately from the lack of adequate transit services. By pinpointing these routes, the model can ensure that critical transit services align with the needs of the most dependent community members. This strategic approach supports essential mobility and access to resources, promoting urban transit equity.

Keywords

essential transit services emergency planning transit dependence equity

The management of public transit encompasses numerous complex factors, requiring planners to assess passenger demand, understand travel patterns, and examine the existing infrastructure. They must consider constraints such as the current levels of supply, including factors like labor, capital, and the number of operational vehicles, among other components of the supply side. Each of these components contributes to the overarching goals of maximizing operational efficiency, minimizing travel times, reducing costs, and enhancing overall service quality. However, what these metrics often fail to capture is the importance of examining the problem through a humane lens—ensuring that public transit services reach those who are most reliant on it.

Identifying the primary beneficiaries of public transit is critical, with various studies highlighting the characteristics of transit-dependent populations. This group largely consists of individuals with lower educational backgrounds, lower income, or other minority groups such as people who identify as Black or have a disability ( 1 , 2 ). Fostering equity when developing a transportation system is crucial for creating sustainable cities and regions. Such efforts contribute to the formation of cohesive societies in which no social group is discriminated against or excluded from access to primary and secondary activities ( 3 ). Although there are examples of social equity objectives in several urban transportation plans, in numerous cases, these goals are not translated into clearly specified objectives, and appropriate indicators to track or assess these objectives are often lacking ( 4 ). Furthermore, the issue of providing equitable services was exacerbated by the COVID-19 pandemic. Essential workers, who share demographic similarities with transit-dependent individuals, were forced to return to work even when the public transit routes they use were canceled. Given that disparity, this paper addresses the following questions: 1) How can public transit authorities effectively manage and prioritize routes to reflect both significant need (demand) and resource availability (supply)? 2) Were transit-dependent individuals adequately served during the early stages of the COVID-19 pandemic?

The authors constructed a method that utilizes local demographics and daily demand and supply data, to provide transit authorities with next-day operational recommendations. This method was tested against the major operational changes made by the Capital Metro Transit Authority (CapMetro) in the Austin, TX area during the COVID-19 pandemic, specifically from March to August 2020. Moreover, this study proposes an alternative data collection method, involving the extraction of demographic variables within a 0.25-mi radius around each route’s stop locations, given that CapMetro does not collect daily rider demographics. This method serves as a viable alternative to traditional onboard surveys.

Background

Societal Role of Public Transportation

Public transportation is indispensable to society, offering relatively affordable and accessible travel options that enable individuals to access job opportunities, explore their communities, connect with loved ones, and pursue personal interests with fewer financial barriers compared with other modes of transportation. The freedom of movement granted by public transportation enhances the lives of individuals while also contributing to the overall well-being of urban society, generating positive impacts on local economies, social cohesion, and environmental sustainability ( 5 ). For some individuals, particularly in urban areas, public transportation plays a crucial role in their daily travel routines. Notably, lower income individuals, as well as those who identify as Black, Hispanic, as well as immigrants, or people under 50, are particularly reliant on public transit for their commuting needs ( 6 ). This reliance may stem from factors such as living in an urban environment, limited access to personal vehicles (commonly referred to as captive ridership), leading to a higher likelihood of using public transit for commuting purposes ( 7 ). Furthermore, disparities in residential proximity to workplaces, especially prevalent among Black and Hispanic populations, often necessitate the use of public transportation owing to longer commuting distances that may not be conducive to walking or biking ( 8 , 9 ).

Equity in Transit Planning: Navigating the Elusive Metrics

Equity in public transport refers to the impartial distribution of the benefits and burdens resulting from transit services, investments, and decisions, ensuring all segments of society are treated fairly, (i.e., without favoritism or prejudice). Understanding the demographic composition of public transit users is a key aspect of equitable transportation planning and has significant implications for both projects and policies ( 10 ). Unfortunately, equity considerations are often overlooked and underestimated, with public transit planning typically prioritizing economic impacts such as local business revenue ( 11 – 13 ). Furthermore, the absence of frequent-ridership demographic measurements hampers transit authorities’ ability to address real-time equity gaps. A reason for this lack of data is the inefficiencies associated with onboard surveys. This method is notably laborious and may require a timeframe between 5 and 10 years for the data to be updated ( 14 , 15 ). Although arguments could be made that major population changes typically occur over longer periods (no less than 10 years), instances of significant changes have occurred rapidly, as witnessed in the city of Austin. Its appeal, including a strong economy, diverse culture, and competitive cost of living, has led to growth rates as high as 6%, adding 138,000 residents in as short a span as 2 years between 2020 and 2022 ( 15 – 17 ). Around 40% of the total growth can be attributed non-Hispanic White people, which is not reflective of the state or country’s population growth, which is mostly composed of people of color ( 16 ). This alludes to the increase in tech and finance jobs within the city, known to result in a more homogenous and less diverse workforce ( 16 ). Without consistently updated demographic data, CapMetro planners would have been unlikely to have perceived the gentrification of East Austin, an area that once comprised a Black majority ( 18 ). Conversely, unconventional methods, such as location-based service (LBS) data collection from cellphones and public transit mobile applications present a promising yet underutilized data acquisition alternative ( 14 ). Without a regular and comprehensive assessment of rider demographics, the challenges and barriers that currently exist for certain socioeconomic groups will persist and deepen, particularly under high-stress conditions such as those experienced during the COVID-19 pandemic.

Exacerbating the Equity Gap

As the COVID-19 virus spread, many people sheltered in place or transitioned to remote work, but essential workers, did not have the luxury of such options. To maintain critical functions across the United States, the Centers for Disease Control and Prevention designated certain workers as essential, allowing them to continue their operations, typically in person, to sustain the broader community ( 19 ). These essential workers span various sectors, including healthcare, law enforcement, transportation, logistics, and public works. A recent analysis of 2018 census data revealed that essential workers are predominantly non-White, have low incomes, and constitute approximately 36% of total transit passengers in the United States ( 1 , 20 ). Brough et al. conducted a study in King County, Seattle, WA examining how changes in travel behavior during the COVID-19 pandemic varied across socioeconomic statuses ( 21 ). They documented travel changes in response to the pandemic, lockdown, and subsequent reopening period using anonymized geolocated cell phone data from SafeGraph Inc. and sociodemographic information from household demographic survey data. The study found that as the economy reopened, higher income individuals, who were more likely to work from home, continued to do so. In contrast, lower income individuals, unable to work remotely, resumed commuting regularly, despite reduced transit services ( 21 ). In Denver, CO, transit ridership actually increased over the pandemic in neighborhoods with relatively low incomes (median household incomes of $35,000 or less), whereas it declined in wealthier neighborhoods (median household incomes of $55,000 or more) ( 22 ).

Public Transit Pandemic Scramble

Facing a significant decline in demand and revenue, transit authorities across the country were forced to implement service reductions, with methods varying from state to state. For instance, the Maryland Transit Administration (MTA) initially proposed eliminating 25 bus lines in the Baltimore area and reducing service on 12 more to cut costs by 20%. However, this plan sparked controversy. Baltimore’s mayor, city council president, and the county executives of three surrounding counties expressed concern that the cuts would disproportionately affect low-income, Black, and Hispanic residents, particularly those in historically disinvested neighborhoods. As a result of the backlash, the MTA opted to cut commuter rail and longer-distance bus routes instead ( 23 , 24 ).

Other transit agencies attempted to strike a balance between cost-cutting measures and maintaining essential services for frequent riders and job destinations during the pandemic ( 2 , 24 ). For example, before implementing operational changes, the Massachusetts Bay Transportation Authority in Boston conducted a thorough review to assess how various service cuts would affect different types of riders. They discovered that over a third of the riders on its Green Line came from households without cars, in contrast to only 1% on a commuter rail line serving several South Shore communities, which is composed of a mix of middle- to upper-middle-class residents, as well as some wealthier individuals, particularly in the more coastal areas ( 24 ). Similarly, the Port Authority, serving the Pittsburgh metropolitan area, opted to increase bus and rail frequencies in 37% of the neighborhoods in its service area. These neighborhoods have a higher proportion of people of color, lower median household incomes, more residents with incomes below the federal poverty line, and a greater percentage of households without vehicles compared with neighborhoods targeted for reduced frequencies ( 22 ). Meanwhile, CapMetro in Austin, TX primarily considered demand levels and routes connecting suburban areas to downtown cores or tech hubs when deciding which routes to maintain, reduce, or suspend ( 25 , 26 ). CapMetro lacks current real-time sociodemographic ridership data and an up-to-date onboard survey. The last survey was conducted in 2015 and a follow-up had been planned for 2020, however, this was disrupted and postponed until 2023 ( 25 , 26 ). Overall, given the multifaceted impacts of service cuts, multiagency cooperation and a holistic consideration of the social impacts of transit service adjustments would be beneficial ( 25 ). This raises the question of how transit authorities can navigate the paradoxical challenge of balancing demand, supply, and equity in public transit services. One potential solution is the analytical hierarchical process (AHP).

Method

This paper presents a method that utilizes AHP to reconcile supply, demand, and equity for transit authorities’ next-day operational recommendations. Illustrated through a case study of CapMetro and the Austin, TX area during the COVID-19 pandemic, this method can be readily applied to routine operations, as it tackles the challenges that were exacerbated by the pandemic and remain relevant today.

Study Area

Table 1 provides basic comparative statistics of the city of Austin to the United States in 2020. Sourced from the American Community Survey (ACS) 2020 census, and the city of Austin Open Data Portal, Austin had a population of 962,000, with a population spread of 3,000 per square mile compared with the national average of 94 per square mile, alluding to its dense urban environment ( 27 , 28 ). In Austin, the percent of people who commute by public transportation is 12%, whereas the national average is 11%, revealing no large deviation between the two. The same could be said for the percent of people considered impoverished with Austin’s population being 12% in poverty compared with the national average of 11%. However, the number of people within the civilian labor force, or the employment rate, is 75%, which is slightly higher than the national average of 63%.

Table 1.

2020 City of Austin Versus U.S. Population Statistics

Area	Population	Population per square mile	Commute to work via public transit, %	Persons in poverty, %	Civilian labor force, %
Austin, TX	962,000	3,000	3	12	75
United States	331,000,000	94	5	11	63

Like many of the transit authorities across the United States, CapMetro also faced the challenges of continuing operations during the COVID-19 pandemic while handling a depleting number of riders and supply line. Their issues along with the practical consideration of data availability, and continuing relations with the university, provided ample reasoning for selecting the CapMetro’s bus network for the case study.

AHP Structure

AHP is a mathematical technique for multicriteria decision making developed by Saaty in the 1970s to facilitate the selection of the best alternative by decision makers ( 29 , 30 ). AHP provides a framework for decomposing and structuring complex problems when decision makers are faced with challenging multicriteria decisions ( 31 , 32 ). Each level of the hierarchy represents a different aspect of the decision, and the AHP uses a series of pairwise comparisons to determine the relative importance of each aspect. This process allows the user to assign priorities to different criteria, then to compare the alternatives based on those priorities. Overall, this allows decision makers to view a complex issue through a more systematic lens rather than a subjective one by comparing alternatives on a ratio scale, as well as permitting the inclusion of quantitative or qualitative data ( 33 ). The four principles in this analysis are as follows ( 31 ):

Structure a hierarchy,

Prioritize alternatives through pairwise comparisons,

Aggregate pairwise assessments to derive a prioritized ranking, and

Check for consistency of preference judgments.

Structuring the hierarchy requires decision makers to clearly define the objective and the decisions that must be made to reach that goal. From there, the alternatives or elements required to reach the final decision must also be considered. Next, the decision and its alternatives are broken down into smaller, more manageable, and homogenous subdecisions. The top level of the hierarchy represents the overall objective, whereas each lower level represents the different aspects, or steps, toward reaching the final goal. Finally, the criteria must be identified to evaluate and prioritize the alternatives ( 34 ). Figure 1 reveals the organization of this research’s hierarchy structure for reaching the goal of delivering efficient and equitable public transit operations. First, researchers must identify the demand, supply, and equitable needs of the network (Criteria A1 to A3). How to measure the criteria against one another is through comparison of the subcriterion factors: B1 to B14. Once compared and weighed, this structure will provide the most essential routes, or alternatives, within the network.

Figure 1.

Analytical hierarchy process structure.

After structuring the hierarchy, the next step involves pairwise comparisons of the criteria. Following the nine-point scale recommended by Saaty, the researchers assessed the relative differences between two elements ( 34 ). A group of expert judges, composed of professionals and academics with experience in public transit management and familiar with local conditions, then assigned values ranging from 1 to 9 when comparing two criteria or subcriteria. Specifically, surveyors were asked how much more important each criterion was when planning next-day operations for public transit routes. A value of 1 indicates equal importance or value, whereas 9 signifies that one criterion is absolutely more important than the other. A total of 10 responses were collected from professors in civil engineering and city regional planning (3 responses), employees from CapMetro (2 responses), employees of Texas Department of Transportation (TxDOT; 2 responses), and from the City of Austin Public Works Department (3 responses). Saaty emphasizes that consistency and expertise outweigh the sheer number of respondents, thereby validating the sample size ( 35 ). CapMetro has the most finite and direct power in transit planning and daily operations, such as creating long-term regional transit plans, and handling the day-to-day transit operations, such as which buses should remain active, the routing and schedule of the buses, and so forth ( 36 ).The City of Austin’s role in transit planning stems from collaborating with CapMetro on improving the current system by expanding the transit network, and identifying those in need of accessible transit ( 37 ). TxDOT checks the performance with measures of effectiveness including the population served and the service effectiveness per capital use ( 38 ).

Once the responses are collected, the pairwise comparisons are organized into a square matrix, which is then synthesized to calculate the relative priority of each factor ( 39 ). AHP calculates the principal eigenvalue and the corresponding normalized right eigenvector of the comparison matrix for each criterion and subcriterion ( 31 , 39 ). Equation 1 provides an explanation of this process.

[\begin{matrix} \begin{matrix} a_{11} \dots a_{1 n} \\ ⋮ ⋮ \\ a_{n 1} \dots a_{nn} \end{matrix} \end{matrix}] [\begin{matrix} w_{1} \\ ⋮ \\ w_{n} \end{matrix}] = λ [\begin{matrix} w_{1} \\ ⋮ \\ w_{n} \end{matrix}] or Ax = λ x

(1)

Vector $x$ = the eigenvector of the pairwise matrix $A$ with the eigenvalue of $λ$ ;

$a_{11}$ = relative value of criterion i to criterion $j$ ; and

Vector $w$ = $(w_{1}, \dots, w_{n})$ = the priority value of criterion $1$ to $n .$

The collected responses must be checked for consistency by using the consistency ratio ( $CR$ ). This ratio compares the consistency index ( $CI$ ) to the random consistency index ( $RI$ ), as shown in Equation 2. The value of $RI$ represents the average consistency index values obtained from randomly generated pairwise comparison matrices of different sizes, specified and generated by Saaty and various researchers ( 34 ).

CR = \frac{CI}{RI}

(2)

The $CI$ quantifies the degree of inconsistency in pairwise comparisons made by the experts. As shown in Equation 3, it measures the deviation from perfect consistency by subtracting the principal eigenvalue of the pairwise comparison matrix ( $λ_{\max}$ ) from the number of criteria being compared ( $n$ ), and then dividing by the number of criteria minus 1.

CI = \frac{λ_{\max} - n}{n - 1}

(3)

A matrix is considered consistent only if $CR \leq 0.1$ ( 34 ). After checking the respondents’ consistency, the priority rankings were then multiplied by the collected criterion data for each route for each day, revealing the most essential routes for next-day operations. However, this paper focuses on the AHP portion of the method rather than the next-day predictions of criterion levels, and therefore provides past historical data.

Factor Analysis

Level 3 of the AHP structure, specifically the subcriteria of “income and social class” and “mobility needs and accessibility” were highly correlated to one another. This posed a problem as the experts were asked to assume the variables were independent of one another as they weighed them against one another. To further confirm that accuracy of the expert rankings and remediate the potential issues of bias and loss of discrimination between the variables, the researchers conducted a factor analysis. Factor analysis is a branch of multivariate analysis that was specifically developed for large sets of correlated variables ( 40 ). This method was used as a means to examine and describe the internal structure of the covariance and correlation matrices of concern ( 40 ). The basic assumption of factor analysis is:

x_{i} = \sum_{r = 1}^{k} l_{ir} f_{r} + e_{i} (i = 1, 2, \dots . p),

(4)

where

$x_{i}$ = observed variable $i$ ( $i$ ranges from 1 to $p$ );

$k$ = number of latent factors (unobserved variables) that are assumed to influence the observed variables;

$l_{ir}$ = factor loading for observed variable $i$ on latent factor $r$ . This indicates how much of the variance in the observed variable $x_{i}$ is explained by the latent factor $f_{r}$ ;

$f_{r}$ = latent factor $r$ (where $r$ ranges from 1 to $k$ ); and

$e_{i}$ = unique error term associated with observed variable $x_{i}$ . This captures the variance in the observed variable that cannot be explained by the latent factors.

After collecting the correlation matrix of the variables within income and social class, and mobility needs and accessibility, the researchers were then able to process the factor analysis and select two factors based on the criterion that both eigenvalues are more significant than one, as shown in Table 2. Furthermore, Table 2 provides the variance, which represents the amount of variance explained by each factor, whereas cumulative variance indicates the total variance explained by the selected factors combined, helping to assess the overall explanatory power of the factor model ( 41 ).

Table 2.

Factor Eigenvalue Variance

Metrics	Factor 1: income	Factor 2: mobility
Eigenvalue	6.908	1.870
Variance, %	0.691	0.187
Cumulative variance, %	0.691	0.878

Next, the researchers employed orthogonal varimax rotation to calculate the factor loadings of the criteria concerning the extracted factors, as shown in Table 3. Varimax rotation is an orthogonal method in factor analysis that maximizes the variance of factor loadings, simplifying factor interpretation by ensuring that each observed variable loads highly on one factor while minimizing loadings on others ( 42 , 43 ). The two factors were composed of the Top 5 criteria loadings, which can be interpreted as the major influences for the factors. For example, the major influences on mobility included disabled individuals, foreign-born residents of the United States, and people aged 5 to 9. The factor loadings were then normalized and compared with the AHP expert rankings. Because the expert rankings matched the order of all criteria, we concluded that our AHP model rankings were not influenced by correlation bias.

Table 3.

Criteria Factor Loadings

Criteria	Factor 1: income	Normalized factor loadings	AHP expert rankings
Transit user	0.967	0.050	0.100
Car ownership	0.936	0.082	0.163
Student	0.931	0.111	0.222
Unemployed	0.914	0.104	0.207
Low income	0.902	0.095	0.190
Criteria	Factor 2: mobility	Normalized factor loadings	AHP expert rankings
Distance to CBD	0.664	0.190	0.380
Disabled	0.545	0.130	0.260
Migration status	0.536	0.060	0.120
Youth	0.508	0.109	0.218
Senior	0.471	0.065	0.130

Note: AHP = analytical hierarchy process; CBD = central business district.

Criteria Data Collection and Pandemic Operations

The data required for the AHP model encompass demand, supply, and equity metrics. Captured by CapMetro, daily demand and supply values for the entire bus network over 3.5 years (January 1, 2019 to May 31, 2022) were provided. Equity data were extracted from census block groups within the city of Austin. The following sections will justify, explain, and describe the extraction process behind the selected criteria and subcriteria. Although the AHP model was applied to the entire bus transit network and timeframe, its focus lay primarily on elucidating operational disparities during the pandemic and the AHP’s recommendations during the initial 5 months (March to August 2020) of the pandemic, as this was when CapMetro went through major operational changes. The following sections delve into the intricacies of these operational shifts.

Data Sources and Description

The authors defined demand by daily ridership levels per route using the common measurements for transit operational efficiency, whereas the transit system supply operational efficiency was defined in relation to labor (total operation time per day), capital (number of operating buses per day), and energy (measure of fuel consumed measured in units of total operating mileage per day) ( 44 – 46 ).

The authors considered 10 distinct demographic factors, all widely recognized in literature as robust measures of transit-dependency. Here, the term “dependence” is used to describe individuals whose primary mode of travel is bus transit, aligning with terminology from previous research. This usage is not intended to imply helplessness ( 47 , 48 ). A comprehensive list of major transit-dependent indicators was curated and organized into two subcriteria: 1) income and social class, and 2) mobility needs and accessibility following guidance from Aman et al. ( 12 ). The indicators gathered for this study included low income, student status, absence of personal vehicle ownership, unemployment status, immigration status, age brackets of youth and senior citizens, transit usage, distance from the central business district, and disability status ( 12 ).

However, it is important to note that certain variables, particularly disability status, often have high margins of error at the block group level, especially in small geographic areas. This can lead to inaccuracies in the representation of transit-dependent populations, potentially skewing the equitable needs assessment. As a result, the AHP model’s recommendations might under- or overestimate the number of individuals who rely on public transit services owing to a disability. To address these limitations, future research could incorporate alternative data sources or employ statistical methods to minimize the impact of these errors on the overall analysis. Even with these challenges, several of the transit-dependent populations, including individuals with disabilities, were likely to be more adversely affected by the decrease of services during the COVID-19 pandemic, as they were typically labeled as essential workers who were required to physically return to their place of work ( 49 – 51 ).

Sociodemographic data pertaining to equity were geospatially retrieved from the 2015/2019 ACS 5-year estimates for the block groups within the city of Austin leveraging ArcGIS. The researchers decided to utilize ACS data from this timeframe because of the absence of recent survey data, such as the 2020 census, which has not yet been spatially linked. Each variable was extracted within a geospatial radius of 0.25 mi from the analyzed route’s stop locations, a range identified by FHWA as the maximum distance most individuals are willing to walk to reach a transit stop (with a likelihood of above 85%) ( 52 ). The resulting observations per route represent the cumulative demographic composition within the intersected block groups’ buffers. This geospatial extraction method serves as a viable alternative to traditional onboard surveys, particularly advantageous during emergency scenarios such as the COVID-19 pandemic. Table 4 provides a descriptive summary of the AHP subcriteria data collected for each route.

Table 4.

Subcriteria Data Description

Subcriteria	Metric
Demand	Riders/day
Supply
Labor	Total operational time/day
Capital	No. of operating buses/day
Energy	Total operating mileage/day
Equity: income and social class
Low income	No. of households with income <$80,000^a
Car ownership	No. of adults without a personal vehicle
Unemployed	No. of adults not in the labor force
Migration status	No. of adults born outside of the United States
Student	No. of people enrolled in an educational institution: primary and secondary school, university, and technical and advanced further education
Equity: mobility needs and accessibility
Youth	No. of persons 5 to 9 years of age
Senior	No. of persons over 65 years of age
Transit user	No. of people who commute by public transit
Disabled	No. of people who are considered disabled^b
Distance to CBD	No. of stops >5 mi from the CBD

Note: CBD = central business district.

Median income level for Austin area in 2019 ($78,965) ( 55 ).

Following U.S. Census Bureau’s definition of who is considered disabled ( 56 ).

Pandemic Operational Changes

CapMetro operates a total of 80 bus routes, one commuter rail line, as well as paratransit services and a pickup on-demand service. Starting on March 18, 2020, just 5 days after the first confirmed case of COVID-19, CapMetro introduced operational changes to its routes to help curb the spread of the virus. Instead of focusing solely on demand levels and surrounding destinations, this research sought to provide CapMetro, and other transit authorities, with a more humane and wholistic guide to identifying essential bus routes. The AHP model offered a list, ranking the most to least essential bus lines for each day during those first 5 months of the pandemic, based on factors such as ridership data, the closure of key destinations like universities and private employers, and the demographics of the surrounding population.

These changes were categorized into three types: continued, reduced, and canceled. Routes were categorized primarily based on ridership levels, with consideration given to destinations, and then assigned to one of the three operational types ( 55 ). The majority of routes (58%) experienced reduced operations, 37% were canceled, and only 5% continued operations as usual. Most of these changes lasted until August 2020. Figure 2 provides a spatial map illustrating the reduced (yellow), canceled (blue), and continued (green) route operations. This graph also provides the percentage allocation of routes per operational change.

Figure 2.

Routes by operation.

Reduced operations were mainly composed of routes previously categorized as “high-frequency” before the pandemic ( 55 ). Typically, these routes operated throughout the week (Monday to Sunday) with a passenger wait time of 10 min, however, during the pandemic, CapMetro reduced service to Sunday levels. Sunday-level service entails fewer trips throughout the day with longer intervals between trips and/or adjusted start and end times (i.e., increase to a 15 min passenger wait time) ( 55 ). Several of these routes served downtown areas, community colleges, and the University of Texas (UT) at Austin. Major destinations along these routes included grocery stores, a public library, a shopping center, two community colleges, an elementary and middle school, a hospital, and a medical center.

The canceled route operations were entirely suspended, notably UT Shuttle buses and express routes. These cancellations were justified by UT’s transition to remote learning and the shift of downtown businesses to remote work ( 55 ). Major destinations along these routes included corporate offices, grocery stores, shops, a public library, UT Austin, City Hall, and the city of Leander, located northwest of Austin.

Lastly, continued operations referred to routes that maintained pre-COVID-19 service levels, operating at typical arrival frequencies. Most of these routes were local buses, with some operating once a week, except for Route 490, which operated twice a week. Major destinations along these routes included grocery stores, affordable housing, and food pantries. These destinations included grocery stores, recreational facilities, an elementary school, neighborhood resource centers (i.e., food pantries), and affordable apartment complexes owned by the City of Austin.

Data Analysis

Priority Ranking

After ensuring the consistency of the expert responses ( $n$ = 10 responses), exhibited a $CR$ value of less than 0.10, the final priority rankings were produced (Figure 3). Utilizing the average of the experts’ rankings, equity emerged as the most significant (0.39) among the Level 2 criteria, surpassing both supply (0.31) and demand (0.30). This indicated that the experts believed that providing equitable transit services should be the priority and major concern when considering daily transit operations.

Figure 3.

Criteria ranking box and whisker plots: (a) Level 2 priority ranking, (b) Level 3 supply priority ranking, (c) Level 3 income and social class priority ranking, and (d) Level 3 mobility and access priority ranking.

Within the Level 3 supply subcriteria, respondents assigned the highest weight to capital (0.38), defined here as the number of buses CapMetro could place in service each day. The emphasis mirrored actions taken nationwide at the height of COVID-19: Houston ran only 50% of its prepandemic schedule, whereas New York, Washington D.C., Phoenix, Minneapolis, and Baltimore operated at roughly 25% ( 56 ). Agencies deliberately trimmed service to curb spread of the virus and to cut costs. Despite the service cuts, agencies still needed to maintain their fleet in operational condition and take delivery of vehicles already on order. Global supply-chain disruptions in 2021 to 2022 delayed deliveries of essential components, such as microchips, HVAC (heating, ventilation, and air-conditioning) units, and brake assemblies, while the cost of a truck or bus body increased by 14.6% within a year. ( 57 ). These shortages slowed routine maintenance and hampered agencies’ ability to restore service once ridership rebounded, explaining why the expert panel still ranked capital highest despite the lower daily deployment. The remaining supply priorities were energy, defined as the total vehicle miles operated per day (0.34), and labor, representing the total operator hours per day (0.28). These lower weights suggest that during the crisis, the availability of buses had a greater impact on service than fuel or driver availability.

With regard to income and social class, students (0.22) and non-U.S. citizens by birth (∼0.22) were identified as the most critical individuals. Several studies point to the strong, positive relationship of students to public transit ridership, especially college students ( 58 , 59 ). During the first 5 months of the pandemic (March through July 2020), public transit was essential for students who relied on these services, particularly as many faced disruptions in access to essential destinations. Although most public transit services were restored by August 1, 2020 the need for reliable transit became even more critical as schools began reopening for the 2020/21 academic year. Providing public transit during the pandemic was especially crucial for students living in Texas. Despite the widely documented surge in COVID-19 cases in Texas during the summer of 2020, about two-thirds of school districts in Texas reopened schools in 2020/21 within 1 week of the start date of 2019/20. Moreover, less than 2% of school districts delayed reopening by more than 8 weeks, possibly in part because of the state’s requirements for obtaining exemptions to remain virtual longer than 8 weeks ( 60 ). Over 90% of Texas school districts opened fully in-person without any staggered or phased-in attendance, in contrast to 42% nationally, further reinforcing the need of public transit services for students during a time of crisis ( 61 ).

In considering mobility and access, individuals residing more than 5 mi from the CBD were deemed the highest priority (0.38), followed by youths aged 5 to 9 years (0.26). CBDs are commercial and economic hubs for cities, and serve as primary centers for business activities, commerce, and employment opportunities within urban environments. Therefore, providing accessibility to CBDs via public transit for those who live farther away is essential because it facilitates equitable access to economic opportunities, essential services, and social activities. For individuals residing in outlying areas, reliable public transit connections to the CBD can significantly reduce transportation barriers, enabling them to access employment centers, educational institutions, healthcare facilities, and cultural amenities without the necessity of private vehicles ( 62 ). However, note that some households intentionally choose peripheral residences because they favor private-car travel, meaning limited transit may not represent a barrier for these residents.

Lastly, it is important to note that the model was developed for the routes that CapMetro currently operates. If the agency later adds or removes a route, the priority order could shift, a phenomenon known as rank reversal. To avoid this, the weights should be recalculated using the ideal normalization procedure. When a new route is introduced, the original weights are renormalized across the larger set of n+1 alternatives; when a route is withdrawn, the local priorities of the remaining routes remain the same, so their composite weights do not change and the ranking is preserved ( 65 ).

Demand

To comprehend the significant shifts in demand levels during the pandemic, the researchers analyzed the daily demand data spanning 3.5 years (January 1, 2019 to May 31, 2022) and identified the Top 5 routes with both the highest ridership and the most stable, consistent demand throughout the period. These routes, listed in order from highest to lowest demand, were Routes 801, 300, 10, 20, and 7. (In Figure 4, all other routes are shaded gray.) Figure 4 reveals that across the network, demand levels plummeted by nearly 50% of their previous levels following confirmation of the first COVID-19 case in Travis County, TX on March 13, 2020 (depicted by the red dashed line). Subsequently, CapMetro implemented major operational changes: continuations, cancellations, or reductions in services, starting on March 18, 2020 (highlighted in green). This decline in demand persisted for 5 months of CapMetro’s operational adjustments until August 1, 2020, when operations returned to pre-COVID levels, accompanied by a slight increase in demand. Once again, the March to August 2020 operational timeframe was utilized for the AHP model, as this encapsulated the most significant and transformative operational changes experienced throughout the entirety of the pandemic.

Figure 4.

Demand trends for CapMetro routes (2019 to 2022).

Figure 4 additionally marks the commencement of UT Austin’s fall semester in 2020 (August 26), which contributed to the surge in demand. Although there was a slight increase in demand levels during 2021 and 2022, demand never fully returned to its prepandemic levels and continued to operate at around 55% of prepandemic levels (per 2024). This further emphasizes the necessity for CapMetro to revise their operational strategies, prioritizing the provision of services to those who rely on public transit. Moreover, this method of operational planning aims to encourage the return of individuals who had previously abandoned public transit.

Figure 5 zooms in on the Top 5, highest demand routes during the dates of CapMetro’s major operational changes, all of which continued to dominate demand even after CapMetro reduced the level of service. Routes 801 and 300 consistently demonstrated higher daily demand levels during CapMetro’s operational adjustments, whereas Routes 10, 20, and 7 experienced demand levels approximately half the size by comparison. Each route experienced peak demand on weekdays, followed by a decline during weekends, indicating the route’s necessity for commuting to work or class. Given that these routes underwent a reduced operation change to a Sunday-level service, that is, fewer trips throughout the day with longer intervals between trips and/or adjusted start and end times, passengers were compelled to endure extended waiting periods for their bus arrivals.

Figure 5.

Top five largest demands during CapMetro operational changes (March 1 to August 1, 2020).

Route 801 was previously designated as a high-frequency and rapid bus route, offering frequent service every 15 min or better, with limited stops. During weekdays, passengers can expect a 10 to 15-min wait, whereas weekends entail a 15 to 20-min wait. Route 801 connects Tech Ridge to Southpark Meadows via UT and downtown, traversing North Lamar and South Congress ( 63 ). In contrast, Route 300 operated as a high-frequency route with more stops, traveling from North Highlands in central Austin, then eastward, bypassing downtown to the Govalle residential area, before returning to the South Lamar shopping district. Routes 10, 20, and 7 also operated as high-frequency routes primarily running north and south through the UT campus ( 64 ).

Similar to demand levels, supply also exhibited a nearly halved reduction in prepandemic labor, energy, and capital levels. Before the pandemic, supply levels were relatively consistent throughout the weekdays, with Monday to Friday exhibiting the largest allocation of supply, and lower levels observed during Saturday and Sunday. These temporal changes essentially flattened to half of the average operating levels after the start of the pandemic. Supply levels began to gradually, but not fully, return to prepandemic levels around June 2021, with Route 801 consuming the most supply in all categories.

Based on the extreme trend changes for both demand and supply, their daily sensitivity to changes such as the pandemic and the supply chain, as well as the lack of return to prepandemic ridership levels, it is more important than ever to employ a robust model that can account for all these variables and provide a straightforward guide/list of the most essential routes for daily operations. Furthermore, this underscores the importance of incorporating equity as a metric. With both demand and supply essentially flattening, CapMetro would have been unable to discern which routes should be prioritized. By prioritizing local users, as indicated by the equity metrics listed in Table 4, CapMetro can then approach operations management not only with a robust model but also with one that integrates a layer of humanity.

Moreover, the analysis of demand levels led the researchers to focus on daily demand across the entire bus route network from March 1 to August 1, 2020. This timeframe allowed the researchers to gauge demand across the network throughout the full spectrum of the pandemic stages, from before the declaration of an emergency to 5 months into CapMetro’s operational changes. This provided further insights into CapMetro’s experiences and observations, helping the researchers assess how CapMetro managed the situation and how routes could have been reprioritized.

Limitations

The analysis relied on census block groups, the smallest units available at the time. Because several blocks lay only partly within the study buffer, the population was assumed to be evenly distributed and allocated according to the share of each block that fell inside the buffer. Demographic characteristics came from the 2019 ACS, and the study assumed that these values did not change appreciably in 2020, given the timing of the work and the delayed release of the updated census.

Demand data introduced an additional constraint. Ridership was recorded as a daily total for each route rather than at the individual stop level, and stop-by-stop figures could not be collected within the project’s technical and time limits. As a result, demand was allowed to vary only over time, not across stops on the same route.

The expert panel also shaped the results. Two of the 10 respondents were CapMetro planners who managed the service during the pandemic, whereas the other eight represented the City of Austin, TxDOT, and academia. This composition broadened the range of perspectives and reduced the chance that the AHP merely reproduced CapMetro’s earlier decisions. Any differences between CapMetro’s 2020 actions and the AHP rankings most likely reflected these additional viewpoints or shifts in planner priorities once the immediate crisis had eased. The questionnaire did not ask why each expert chose the specific weights allocated, so explaining those shifts lies beyond the scope of this study and remains a topic for future qualitative work.

Methodological limitations also include the potential for rank reversal. The study used the conventional eigenvector form of AHP, which fixes priorities for the current set of routes. If CapMetro later adds or removes a route, the hierarchy should be rebuilt and the pairwise comparisons repeated, or the weights should be recomputed with the ideal normalization mode to preserve the established ranking.

Finally, the criteria weights reflected Austin’s travel patterns, network design, and expert judgments. Agencies elsewhere should recalibrate the hierarchy with local specialists, objectives, and data before applying the framework. Future research is encouraged to test the procedure in systems of different sizes and governance structures to examine how priorities vary across contexts.

Results

AHP Overall Level of Need

Applying the final expert criteria rankings (refer to Figure 3) to the entire CapMetro transit network from March 1 (before the first confirmed case of COVID-19 in Travis County, TX) until August 1, 2020 (the conclusion of major operational changes by CapMetro), Figure 6 illustrates the daily level of need for each bus route, ranked from highest to lowest. Need is defined as the final daily level of prioritization for each route, considering all three categories of demand, supply, and equity. Located on the right-hand side of Figure 6 is the list of the Top 20 routes with the highest consistent average level of need, highlighting Route 801 as having the highest need and Route 142 as having the lowest average need during this specific timeframe.

Figure 6.

Analytical hierarchy process of the CapMetro bus network (March 1 to August 1, 2020).

A more digestible format is presented in Figure 7 focusing on March 1 to April 14, 2020. Note that the average level of need shifted after adjusting the analysis timeframe, with Route 661 now included in the list of the Top 20 essential routes in the network. The spikes observed in the routes usually coincide with Sundays, as certain routes are inactive on Sundays, allowing the remaining routes’ final AHP priority scores to rise despite a decrease in both demand and supply. By pinpointing the date of the first confirmed COVID-19 case and CapMetro’s operational changes, it becomes evident that there was a distinct increase in the overall network’s level of need as demand decreased and equitable need rose. Notice the upward trendlines and the shift in the order of the routes’ level of need, such as Route 7 surpassing Routes 300 and 10 following the confirmed case of COVID-19.

Figure 7.

Analytical hierarchy process of CapMetro’s bus network (March 1 to April 14, 2020).

To further understand whether CapMetro’s operational changes reflected the level of need throughout the city during the pandemic, the researchers constructed a graphic of the routes’ postconfirmed COVID-19 case (March 16, 2020). This date was chosen to capture the pandemic’s impacts on the route network, thereby guiding CapMetro on future demand and supply. Figure 8 reveals the breakdown of demand, supply, and equitable need per route. Note that the y-axis in Figure 8, labeled “AHP Priority,” represents the prioritization score assigned to each route based on the AHP. This score is a composite measure derived from multiple criteria, including demand, supply, and equity (such as income/social class and mobility/access factors). It is important to note that this priority score is not a percentage of riders that fit these categories but rather reflects the relative importance of each route according to the AHP model, while considering these factors.

Figure 8.

Most essential routes postpandemic.

In Figure 8, Route 801 was identified as the most essential and Route 233 as the least essential for this day. Among these categories, demand predominated the level of need for Route 801 (as depicted in Figure 5), followed by supply and equity. This figure additionally highlights the routes that were canceled during CapMetro’s major operational changes. For instance, Route 985—an express route allowing riders to utilize the MoPac Express Lanes, thus enabling them to bypass traffic toll-free—operates from the Leander Station Park & Ride into downtown Austin and the UT campus. Despite its cancellation, it ranks among the Top 17 highest needed routes for that day. Following closely behind Route 985 was Route 481 and nine other canceled routes.

If researchers were to follow the structure or percentage breakdown of CapMetro’s continued, reduced, and canceled route operational changes, the routes should have been organized in the order illustrated in Figure 9, based on the level of need throughout the network. Routes 801 and 7 should therefore have continued operations as usual according to their level of need (i.e., the top 5.2% of routes, in other words, the Top 2 out of 80 routes). For routes that should have operated with reduced service, representing the next 57.7% of routes, Routes 300 to 486, based on their priority level, should have been considered. This percentage includes most routes that did not have as high a need as Routes 801 and 7 but still required service. Noticeably, within this group, there were four canceled routes that should have instead operated at a reduced frequency. Lastly, the remaining 37.1% of routes (Routes 233 to 323), with the lowest level of need according to the model, could have been canceled, owing to their significantly lower demand, supply, and equity prioritization.

Figure 9.

Suggested operational changes after March 16, 2020.

Level 1: Priority Ranking

To assess the routes recommended for cancellation, the researchers constructed Figure 10. Each bar represents the route’s overall AHP segments after the first confirmed COVID-19 case, indicating the proportion of each Level 2 criteria category within the total. In other words, it reveals whether demand, supply, or equity dominates the route’s total priority. For instance, Route 485, initially earmarked for cancellation, exhibits a significant proportion of local equitable need (∼78%). This route serves as a local bus route connecting destinations such as the CBD, Dell Children’s Medical Center, Norwood Transit Center, and Walmart. Routes similar to 485, like 483, 484, and 486, with high levels of equitable need, should be considered special exceptions for continuing or reduced operation changes.

Figure 10.

Analytical hierarchy process proportion breakdown per route (March 16, 2020).

Level 2: Supply Priority Ranking

To gain further insight into the levels of supply that CapMetro had allocated to each route after the first confirmed case of COVID-19, researchers constructed Figure 11. This figure presents the AHP priority breakdown for the second-level criteria of supply (i.e., energy, capital, labor), and unlike the proportion breakdown in Figure 10, Figure 11 provides the largest to smallest AHP priority for each route’s supply level and the applicable share of the subcriteria for that particular criterion.

Figure 11

Supply breakdown of post-COVID-19 (March 16, 2020).

It is worth noting that this date was too early for the transit authorities to have fully addressed the supply-chain disruptions. However, by assessing the level of supply needed for each route, transit authorities can streamline planning for next-day operations, such as determining the number of bus drivers required, estimating fuel consumption, and deciding how many buses should operate on each route. For example, we can see that Route 801 demanded the highest level of supply priority, whereas Route 483 required the lowest. The graph also reveals that energy, capital, and labor were not equally distributed across all routes, which could be the result of factors like route length or operation time. Figure 11 also highlights (red asterisks) the routes that were canceled during CapMetro’s major operational changes. Route 985, for instance, was canceled despite being among the Top 10 routes with the highest supply needs, requiring a significant amount of capital (buses) and energy (operating mileage), though relatively less labor (operational time).

Level 3: Income and Social Class Priority Ranking

Income and social class are illustrated in Figure 12. Route 1 exhibited the highest need, serving a significant number of individuals born outside of the United States, whereas Route 237 showed the lowest need within this category. Notably, canceled Route 481 ranked among the Top 9 most needed routes for income and social class, catering to a considerable proportion of people born outside the United States. As evidenced by numerous studies, immigrants in the United States continue to work in high-risk environments, often with limited access to healthcare and economic relief, and facing discrimination ( 65 , 66 ). Furthermore, despite official public health recommendations for stay-at-home orders and social distancing, many immigrants lacked the ability to work remotely during the pandemic, persisting in essential industries such as food services, healthcare, manufacturing, construction, agriculture, and transportation ( 66 ). Further research indicates that both Hispanic and Asian immigrants rely on public transit at rates higher than native-born commuters ( 67 ).

Figure 12.

Income and social class breakdown of post-COVID-19 (March 16, 2020).

Level 4: Mobility and Access Priority Ranking

Figure 13 underscores the results according to mobility and access, once more highlighting Route 1 as exhibiting the highest level of need in this category. This is primarily because of its numerous stops located over 5 mi from the CBD, coupled with the predominant presence of youths among its ridership. Likewise, canceled Route 481, also characterized by a significant number of stops exceeding 5 mi from the CBD and catering to a youth-heavy demographic, would have presented substantial challenges for individuals, particularly those employed in the service industry. This demographic includes workers in restaurants, retail stores, movie theaters, and malls, predominantly situated within the CBD. The discontinuation of Route 481 would have compounded the difficulties faced by these individuals, especially as Governor Greg Abbott began allowing these establishments to reopen with limited capacity from late April 2020 onwards, aiming to jumpstart economic recovery ( 68 ). Furthermore, the absence of Route 481 would have posed significant hurdles for young children aged 5 to 9 years old. With parents unable to rely on this route to transport their children to school once in-person or hybrid classes resumed in fall 2020, these children would have been left without a viable means of transportation to school ( 61 ).

Figure 13.

Mobility and access breakdown of post-COVID-19 (March 16, 2020).

Conclusions

Public transit serves as an essential organ for the societal body, facilitating access to job opportunities, and essential trips throughout the community. Not only does public transit enhance the lives of individuals, but it also contributes to the overall well-being of urban society, generating positive impacts on local economies, social cohesion, and environmental sustainability ( 5 ). Certain demographic groups use public transit more often, notably lower income individuals, as well as those who identify as Black, Hispanic, as well as immigrants, and people under 50 ( 6 ). This reliance often stems from a lack of personal vehicle ownership ( 6 ).

During the COVID-19 pandemic, the social gap among public transit users was further exacerbated, along with the issue of balancing a decline in demand as well as supply ( 57 ). To address the paradoxical issue of balancing the three main components (demand, supply, and equity) for operations planning and next-day operational recommendations for transit authorities, the researchers developed a method utilizing AHP.

Demonstrated through the case study of CapMetro and the Austin, TX area during the COVID-19 pandemic, the researchers applied the AHP model to the entire bus service network, excluding rail and paratransit. The focus was primarily on elucidating operational disparities during the pandemic and the AHP’s recommendations during the initial 5 months (March to August 2020). This period was significant as CapMetro underwent most of its major operational changes then. During this timeframe, CapMetro categorized route operations into three main categories: continued, reduced, or canceled. Routes were primarily categorized based on ridership levels, with consideration given to destinations, and then assigned to one of the three operation types ( 55 ). With this case study, researchers aimed to identify who was left out of these major operational changes, as well as provide alternative operational guidance.

For data, daily demand and supply values were provided by CapMetro, whereas equity data were geospatially retrieved from the 2015/2019 ACS 5-year estimates for the block groups within the city of Austin. The geospatial extraction method, which involves extracting demographic variables from a 0.25-mi radius from each route’s stop location, serves as a viable alternative to traditional onboard surveys. This method is particularly advantageous during emergency scenarios such as the COVID-19 pandemic. In total, the researchers collected 13 subcriterion variables.

After collecting expert rankings of the AHP’s criteria, the average rankings for the Level 2 criteria revealed equity as the most significant (0.39), surpassing both supply (0.31) and demand (0.30). The Level 3 subcriteria rankings for both supply and equity, specifically income and mobility/access, were also ranked (see Figure 3). Applying these rankings to each route’s day of demand, supply, and equity values, planners can identify which routes are considered most to least in need by the community. Furthermore, by applying the percentage breakdown of continued, reduced, and canceled routes to the ordered routes from most to least importance, it was revealed that routes that were canceled during the early phases of the pandemic should have instead reduced their operational frequency (Figure 9).

Researchers also found that the routes that were canceled mainly left immigrants without a means to commute. Reaffirming previous studies that found that immigrants in the United States continued to work in high-risk environments, often with limited access to healthcare and economic relief, and facing discrimination ( 66 ). Additionally, despite official public health recommendations for stay-at-home orders and social distancing, many immigrants lack the ability to work remotely and persisted in essential industries such as food services, healthcare, manufacturing, construction, agriculture, and transportation ( 66 ). Moreover, researchers found that for mobility and accessibility, individuals, particularly those employed in the service industry, who lived 5 mi from the CBD would have faced substantial challenges reaching their jobs after the cancellation of their routes. As Texas reopened as early as April 2020 after the start of the pandemic, the absence of routes would also have posed significant hurdles for young children aged 5 to 9. With parents unable to rely on these routes to transport their children to school once in-person or hybrid classes resumed in fall 2020, these children would have been left without a viable means of transportation to school ( 61 ).

Overall, the AHP method structured by the researchers, which considered daily demand, supply, and equity, could be readily applied to routine operations, as it successfully addressed challenges exacerbated by the pandemic that remain relevant today. By applying this method to the city of Austin, TX, researchers were also able to confirm that those in need, including foreign-born citizens and youths, were not provided public transit service at the beginning of the pandemic, even after the early reopening of the Texas economy and school system.

The potential application of this research lies in improving public transit operations by ensuring equitable access to essential services, especially during emergencies like the COVID-19 pandemic. AHP enables transit authorities to balance demand, supply, and equity, prioritizing vulnerable populations such as low-income and transit-dependent individuals. This method is flexible and can be adopted by any transit authority globally by simply replacing Austin-specific data with their own. Other potential users include departments of transportation (DOTs) and civil consulting firms.

Although this method was applied during the pandemic, it is designed for use at any time, as demand, supply, and equity are always key components of operational decision making. The method is also adaptable to both established and developing transit networks. In the latter case, practitioners could begin by assessing equitable distribution across a city to measure need and then apply demand and supply levels once the lines are implemented.

This approach additionally offers an alternative to time-consuming onboard surveys, which typically require months of work. Instead, transit authorities could use readily available sociodemographic data from the U.S. census (though census data availability may vary by county). However, a basic understanding of ArcGIS Pro software is required, which could be a limitation for low-resourced DOTs. Other limitations include reliance on census data, which may not capture rapid demographic shifts, and the absence of real-time ridership demographics, potentially affecting the accuracy of equity assessments.

Future research could focus on integrating real-time data sources, such as mobile or LBS, to create a more dynamic and responsive approach to transit planning. Moreover, it would be valuable to investigate long-term transit recovery strategies postpandemic, examine the motives and reasoning behind individual weight choices, and explore how equity can be more effectively incorporated into daily operations.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: J. Hall; data collection: J. Hall; analysis and interpretation of results: J. Hall; draft manuscript preparation: J. Hall, C. Sabillion. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Jennifer Hall

Christian A. Sabillon-Orellana

Randy Machemehl

References

Chen

Who Left Riding Transit? Examining Socioeconomic Disparities in the Impact of COVID-19 on Ridership. Transportation Research Part D: Transport and Environment, Vol. 90, 2021, p. 102654. https://doi.org/10.1016/J.TRD.2020.102654.

DeWeese

Hawa

Demyk

Davey

Belikow

El-geneidy

A Tale of 40 Cities: A Preliminary Analysis of Equity Impacts of COVID-19 Service Adjustments across North America. Transport Findings, 2020.

Camporeale

Caggiani

Fonzone

Ottomanelli

Quantifying the Impacts of Horizontal and Vertical Equity in Transit Route Planning. Transportation Planning and Technology, Vol. 40, No. 1, 2017, pp. 28–44. https://doi.org/10.1080/03081060.2016.1238569.

Manaugh

Badami

M. G.

El-Geneidy

A. M.

Integrating Social Equity into Urban Transportation Planning: A Critical Evaluation of Equity Objectives and Measures in Transportation Plans in North America. Transport Policy, Vol. 37, 2015, pp. 167–176. https://doi.org/10.1016/j.tranpol.2014.09.013.

Sun

Cui

Evaluating the Coordinated Development of Economic, Social and Environmental Benefits of Urban Public Transportation Infrastructure: Case Study of Four Chinese Autonomous Municipalities. Transport Policy, Vol. 66, 2018, pp. 116–126. https://doi.org/10.1016/j.tranpol.2018.02.006.

Anderson

Who Relies on Public Transit in the U.S. Pew Research Center, April7, 2016.

FHWA. Household Travel in America. Policy and Governmental Affairs. https://www.fhwa.dot.gov/policy/2010cpr/chap1.cfm. Accessed Mar. 14, 2024.

McKenzie

Modes Less Traveled—Bicycling and Walking to Work in the United States: 2008–2012. U.S. Department of Commerce. American Community Survey Reports, 2014.

Ellis

Wright

Parks

Work Together, Live Apart? Geographies of Racial and Ethnic Segregation at Home and at Work. Annals of the Association of American Geographers, Vol. 94, 2008, pp. 620–637. https://doi.org/10.1111/j.1467-8306.2004.00417.x.

10.

Ben-Elia

Benenson

A Spatially-Explicit Method for Analyzing the Equity of Transit Commuters’ Accessibility. Transportation Research Part A: Policy and Practice, Vol. 120, 2019, pp. 31–42. https://doi.org/10.1016/j.tra.2018.11.017.

11.

Caggiani

Camporeale

Ottomanelli

Facing Equity in Transportation Network Design Problem: A Flexible Constraints Based Model. Transport Policy, Vol. 55, 2017, pp. 9–17. https://doi.org/10.1016/J.TRANPOL.2017.01.003.

12.

Aman

Smith-Colin

Transit Deserts: Equity Analysis of Public Transit Accessibility. Journal of Transport Geography, Vol. 89, 2020, p. 102869. https://doi.org/10.1016/J.JTRANGEO.2020.102869.

13.

Taylor

B. D.

Morris

E. A.

Public Transportation Objectives and Rider Demographics: Are Transit’s Priorities Poor Public Policy?

Transportation, Vol. 42, No. 2, 2015, pp. 347–367. https://doi.org/10.1007/s11116-014-9547-0.

14.

Federal Transit Administration. Location-Based Service Data for Transit Agency Planning and Operations: Market Scan and Feasibility Analysis. Publication 0245. 2023.

15.

Mishalani

R. G.

McCord

M. R.

Transit Passenger Origin–Destination Flow Estimation: Efficiently Combining Onboard Survey and Large Automatic Passenger Count Datasets. Transportation Research Part C: Emerging Technologies, Vol. 58, 2015, pp. 178–192. https://doi.org/10.1016/j.trc.2015.04.021.

16.

Gemenne

Why the Numbers Don’t Add up: A Review of Estimates and Predictions of People Displaced by Environmental Changes. Global Environmental Change, Vol. 21, 2011, pp. S41–S49. https://doi.org/10.1016/j.gloenvcha.2011.09.005.

17.

Stark

. Austin Ranks Second on List of Fastest-Growing US Economies, City Demographer Weighs In. KXAN Austin. https://www.kxan.com/news/local/austin/austin-ranks-second-on-list-of-fastest-growing-us-economies-city-demographer-weighs-in/. Accessed April 15, 2024.

18.

Charpentier

McGlinchy . Two Paragraphs Forced Black Residents to East Austin. Exploding Real Estate Prices Forced Them Out. KUT Radio, Austin’s NPR Station. https://www.kut.org/austin/2023-06-22/two-paragraphs-forced-black-residents-to-east-austin-exploding-real-estate-prices-forced-them-out. Accessed April 15, 2024.

19.

Centers for Disease Control and Prevention. Categories of Essential Workers: COVID-19 Vaccination. Vaccines and Immunizations. https://www.cdc.gov/vaccines/covid-19/categories-essential-workers.html. Accessed Jan. 12, 2023.

20.

TransitCenter. Transit Is Essential: 2.8 Million U.S. Essential Workers Ride Transit to Their Jobs. https://transitcenter.org/2-8-million-u-s-essential-workers-ride-transit-to-their-jobs/. Accessed January 12, 2023.

21.

Brough

Freedman

Phillips

D. C.

Understanding Socioeconomic Disparities in Travel Behavior During the COVID-19 Pandemic. Journal of Regional Science, Vol. 61, No. 4, 2021, pp. 753–774.

22.

Morales-Burnett

Freemark

The Ways Transit Agencies Adapted during the Pandemic Can Inform an Equitable Recovery. Urban Institute. https://www.urban.org/urban-wire/ways-transit-agencies-adapted-during-pandemic-can-inform-equitable-recovery. Accessed November 17, 2022.

23.

Kinnally

County Leaders Express Concerns Over MTA Proposal to Slash Bus, MARC Service. Conduit Street. https://conduitstreet.mdcounties.org/2020/09/02/county-leaders-express-concerns-over-mta-proposal-to-slash-bus-marc-service/. Accessed Jan. 10, 2023.

24.

Vock

D. C.

Transit in Crisis. American Planning Association. https://www.planning.org/planning/2020/dec/transit-in-crisis/. Accessed Jan. 10, 2023.

25.

Lovings

Email Interview with CapMetro Regarding Operation Changes and Data Requests during the COVID-19 Pandemic. May30, 2022.

26.

Hall

Sabillon-Orellana

C. A.

Machemehl

A Methodology for Choosing Transit Service Reductions during the COVID-19 Pandemic: A Question of Equity. Proceedings of the Transportation Research Board Annual Meeting, 2023, pp. 600–611. https://doi.org/10.1061/9780784484883.052.

27.

United States Census Bureau. U.S. Census Bureau QuickFacts: Austin City, Texas; United States. https://www.census.gov/quickfacts/fact/table/austincitytexas,US/POP010220. Accessed September 29, 2024.

28.

City of Austin. Percent Split of Modes Based on Commute to Work (Mode Share). Open Data Portal. https://data.austintexas.gov/stories/s/M-A-1-Percent-split-of-modes-based-on-commute-to-w/hm3r-8jfy/. Accessed September 29, 2024.

29.

Saaty

R. W.

The Analytic Hierarchy Process—What It Is and How It Is Used. Mathematical Modelling, Vol. 9, No. 3, 1987, pp. 161–176. https://doi.org/10.1016/0270-0255(87)90473-8.

30.

Saaty

T. L.

Highlights and Critical Points in the Theory and Application of the Analytic Hierarchy Process. European Journal of Operational Research, Vol. 74, No. 3, 1994, pp. 426–447.

31.

Porras-Alvarado

J. D.

Murphy

M. R.

Han

Zhang

Arellano

Analytical Hierarchy Process to Improve Project Prioritization in the Austin District, Texas. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2613, No. 1, 2017, pp. 29–36. https://doi.org/10.3141/2613-04.

32.

Zhang

Machemehl

R. B.

Ahson

Application of the Analytic Heirarchy Process for Prioritization of Pavement Data Collection for the TXDOT PMIS. Publication 0–4186. 2004.

33.

Saaty

T. L.

Absolute and Relative Measurement with the AHP. The Most Livable Cities in the United States. Socio-Economic Planning Sciences, Vol. 20, No. 6, 1986, pp. 327–331. https://doi.org/10.1016/0038-0121(86)90043-1.

34.

Saaty

T. L.

Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, 2000.

35.

Sagir Ozdemir

Saaty

How Many Judges Should There Be in a Group ?

Annals of Data Science, Vol. 1, 2015, pp. 359–368. https://doi.org/10.1007/s40745-014-0026-4.

36.

Transit Plan 2035. https://www.capmetro.org/transit-plan-2035. Accessed September 29, 2024.

37.

City of Austin. Transit Enhancement Infrastructure Report. https://www.austintexas.gov/page/transit-enhancement-infrastructure-report, https://www.austintexas.gov/page/transit-enhancement-infrastructure-report. Accessed September 29, 2024.

38.

Texas Department of State Health Services. Planning Our Transit Future. http://txdot.gov/en/home/about/newsroom/statewide/planning-our-transit-future.html. Accessed September 29, 2024.

39.

Brunner

Kim

Yamashita

Analytic Hierarchy Process and Geographic Information Systems to Identify Optimal Transit Alignments. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2215: pp. 1–112.

40.

Lawley

D. N.

Maxwell

A. E.

Factor Analysis as a Statistical Method. Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 12, No. 3, 1962, pp. 209–229. https://doi.org/10.2307/2986915.

41.

Lorenzo-Seva

. How to Report the Percentage of Explained Common Variance in Exploratory Factor Analysis. Department of Psychology, Universitat Rovira i Virgili, Tarragona.

42.

Dilbeck

K. E.

The SAGE Encyclopedia of Communication Research Methods. SAGE Publications, Inc, Thousand Oaks, CA, 2017.

43.

Huang

J.-J

. Analytic Hierarchy Process with the Correlation Effect via WordNet. Mathematics, Vol. 9, No. 8, 2021, p. 872. https://doi.org/10.3390/math9080872.

44.

Chu

Fielding

G. J.

Lamar

B. W.

Measuring Transit Performance Using Data Envelopment Analysis - ScienceDirect. Transportation Research Part A: Policy and Practice, Vol. 26, No. 3, 1992, pp. 223–230. https://doi-org.ezproxy.lib.utexas.edu/10.1016/0965-8564(92)90033-4.

45.

Lao

Liu

Performance Evaluation of Bus Lines with Data Envelopment Analysis and Geographic Information Systems. Computers, Environment and Urban Systems, Vol. 33, No. 4, 2009, pp. 247–255. https://doi.org/10.1016/j.compenvurbsys.2009.01.005.

46.

Wei

Liu

Wang

Golub

Farber

Evaluating Public Transit Services for Operational Efficiency and Access Equity. Journal of Transport Geography, Vol. 65, 2017, pp. 70–79. https://doi.org/10.1016/j.jtrangeo.2017.10.010.

47.

Guerra

What the Heck Is a Choice Rider? A Theoretical Framework and Empirical Model. Journal of Transport and Land Use, Vol. 15, No. 1, 2022, pp. 165–182. https://doi.org/10.5198/jtlu.2022.2096.

48.

Wilson

. ‘Human Transit’ 2.0: How Mass Modes Make Us More Free. Streetsblog USA. https://usa.streetsblog.org/2024/02/08/jarrett-walker-on-the-updated-human-transit-and-how-mass-modes-make-us-more-free. Accessed September 27, 2024.

49.

Rowangould

Karner

Palm

LaRue

Covid-19 Pandemic Impacts on Essential Transit Riders: Findings from a U.S. Survey. Transportation Research. Part D, Transport and Environment, Vol. 105, 2022, p. 103217. https://doi.org/10.1016/j.trd.2022.103217.

50.

Parker

M. E. G.

Bouzaghrane

M. A.

Obeid

Hayes

Frick

K. T.

Rodríguez

D. A.

Sengupta

Walker

Chatman

D. G.

Public Transit Use in the United States in the Era of COVID-19: Transit Riders’ Travel Behavior in the COVID-19 Impact and Recovery Period. Transport Policy, Vol. 111, 2021, pp. 53–62. https://doi.org/10.1016/j.tranpol.2021.07.005.

51.

Prassl

A. A.-

Boneva

Golin

Rauh

Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys. Journal of Public Economics, Vol. 189, No. 104245, 2020, pp. 104–245. https://doi-org.ezproxy.lib.utexas.edu/10.1016/j.jpubeco.2020.104245.

52.

FHWA. Chapter 4: Actions to Increase the Safety of Pedestrians Accessing Transit. Pedestrian & Bicycle Safety. https://safety.fhwa.dot.gov/ped_bike/ped_transit/ped_transguide/ch4.cfm. Accessed Mar. 26, 2024.

53.

U.S. Census Bureau. QuickFacts: Austin City, Texas. https://www.census.gov/quickfacts/fact/table/austincitytexas/IPE120222. Accessed March 26, 2024.

54.

U.S. Census Bureau. American Community Survey and Puerto Rico Community Survey 2021 Subject Definitions, U.S. Census Bureau, Washington, D.C. 2021.

55.

Lovings

B. J

. Capital Metro Data Analyst. Personal Communication, 2022.

56.

Liu

Tao

Zhao

Impacts of COVID-19 on Public Transit Ridership. International Journal of Transportation Science and Technology, Vol. 12, No. 1, 2023, pp. 34–45. https://doi.org/10.1016/j.ijtst.2021.11.003.

57.

American Public Transportation Association (APTA). Supply Chain, Inflation, and Workforce Challenges. APTA Fact Sheet. 2022.

58.

Brown

A. E.

Blumenberg

Taylor

B. D.

Ralph

Voulgaris

C. T.

A Taste for Transit? Analyzing Public Transit Use Trends among Youth. Journal of Public Transportation, Vol. 19, No. 1, 2016, pp. 49–67. https://doi.org/10.5038/2375-0901.19.1.4.

59.

Khattak

Wang

Son

Agnello

Travel by University Students in Virginia: Is This Travel Different from Travel by the General Population?

Transportation Research Record: Journal of the Transportation Research Board, Vol. 2255, No. 1, 2011, pp. 137–145. https://doi.org/10.3141/2255-15.

60.

Governor Abbott, Lt. Governor Patrick, Speaker Bonnen, Chairman Taylor, Chairman Huberty. Governor Abbott, Lt. Governor Patrick, Speaker Bonnen, Chairman Taylor, Chairman Huberty release statement on school re-openings. Office of the Texas Governor. https://gov.texas.gov/news/post/governor-abbott-lt-governor-patrick-speaker-bonnen-chairman-taylor-chairman-huberty-release-statement-on-school-re-openings. Accessed April 1, 2024.

61.

Courtemanche

Yelowitz

Zimmer

School Reopenings, Mobility, and COVID-19 Spread: Evidence from Texas. Publication w28753. National Bureau of Economic Research, Cambridge, MA, 2021, p. w28753.

62.

Syed

S. T.

Gerber

B. S.

Sharp

L. K.

Traveling Towards Disease: Transportation Barriers to Health Care Access. Journal of community health, Vol. 38, No. 5, 2013, pp. 976–993. https://doi.org/10.1007/s10900-013-9681-1.

63.

Rapid. capmetro. https://capmetro.org/rapid. Accessed April 1, 2024.

64.

Bus. capmetro. https://capmetro.org/ourservices/busroutes. Accessed April 1, 2024.

65.

Wang

Y.-M.

Elhag

T. M. S.

An Approach to Avoiding Rank Reversal in AHP. Decision Support Systems, Vol. 42, No. 3, 2006, pp. 1474–1480. https://doi.org/10.1016/j.dss.2005.12.002.

66.

Đoàn

L. N.

Chong

S. K.

Misra

Kwon

S. C.

S. S.

Immigrant Communities and COVID-19: Strengthening the Public Health Response. American Journal of Public Health, Vol. 111, No. Suppl 3, 2021, pp. S224–S231. https://doi.org/10.2105/AJPH.2021.306433.

67.

Blumenberg

Evans

A. E.

Planning for Demographic Diversity: The Case of Immigrants and Public Transit. Journal of Public Transportation, Vol. 13, No. 2, 2010, pp. 23–45. https://doi.org/10.5038/2375-0901.13.2.2.

68.

Svitek

Gov. Greg Abbott to Let Restaurants, Movie Theaters and Malls Open with Limited Capacity Friday. The Texas Tribune, April27, 2020.