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Article

Missing Meals and Missed Rides: Transportation Barriers to Food Access for Vulnerable Populations

by
Laura M. Keyes
1,*,
Jintak Kim
1,
Sowmya Balachandran
2,
Sara Kuttler
1 and
Simon Andrew
1
1
Department of Public Administration, University of North Texas, 1155 Union Circle #310617, Denton, TX 76203, USA
2
Department of Urban Planning & Community Development, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA 02135, USA
*
Author to whom correspondence should be addressed.
Urban Sci. 2025, 9(6), 198; https://doi.org/10.3390/urbansci9060198
Submission received: 7 May 2025 / Revised: 23 May 2025 / Accepted: 26 May 2025 / Published: 1 June 2025
Versions Notes

Abstract

:
Food insecurity is not only shaped by behavioral, socioeconomic, and demographic factors but is also determined by an individual’s ability to access food in their community. Analyzing new survey data from a large city located in the southwest USA, this research adds to current dialogues on food insecurity among older adults and individuals with disabilities in economically disadvantaged communities. Using logistic regression, the findings provide nuanced evidence to distinguish between two crucial determinants of food insecurity related to transportation access—the lack of service availability and transportation unaffordability. One-third of respondents missed grocery trips due to a lack of affordable transportation. For individuals who cannot drive or do not own vehicles, access to ride services is critical to overcome exacerbated risks owing to food insecurity compared with those who own personal vehicles. Those relying on community-based ride services are more likely to miss grocery trips due to inadequate services. Our research further provides evidence that inadequate services result in greater food insecurity for specific vulnerable subgroups, such as those with poor health, renters, and those with lower incomes. Our findings highlight the importance of understanding behavioral travel constraints and call for equity-focused improvements in transportation systems to mitigate food access barriers.

1. Introduction

Inadequate transportation options can make it difficult for older adults and individuals with disabilities to access nutritious food [1], especially in low-income neighborhoods or food deserts where the only options are convenience stores, fast food, or gas stations [2,3]. Older adults, individuals ages 65 and older, and individuals of all ages with disabilities experiencing financial burden constitute a transportation-disadvantaged population (TD) [4]. The TD population struggles to access fresh food and becomes food-insecure when transportation is unaffordable [5]. By 2050, food insecurity will impact over 7 million people in this group, with higher representation among Black and Hispanic individuals compared with Whites [6].
Within TD populations, those with very low income, with limited education, or living alone face heightened risks, often having to choose between food and medicine, leading to poor health outcomes [7,8,9]. Individuals with disabilities face a greater risk of food insecurity than those without disabilities [10]. The TD population may be in greater jeopardy of poor physical and mental health outcomes (i.e., due to missed medical care, lack of socialization, and lack of access to food) if they are without reliable transportation access [11]. Kansanga et al. [12] also argue that the TD population without transportation access experiences poorer health outcomes, raising a critical public health concern [13]. The heightened vulnerability and health risks, therefore, make it important to understand the determinants of food access for these populations.
Previous scholars and definitions by the United States Food and Drug Administration have established foundational approaches to understanding food insecurity related to health, nutrition, and access [14,15,16]. The Food and Agriculture Organization offers a multi-dimensional perspective of food access based on availability, access, utilization, and stability pillars [17]. Guine et al. [16] provided a global-scale perspective, suggesting that larger systemic issues create disparities in food access; yet, these problems ultimately directly impact individuals at the local level due to policy fragmentation. In our case, a lack of public transportation infrastructure has a direct impact on vulnerable individuals. Building on our argument to understand food access at the local level, Calloway et al. [14] provided evidence of the importance of additional measures of perceived food availability (at grocery stores or pantries), utilization barriers (such as lack of cooking knowledge and equipment), and stability (access over time) and their association with greater levels of food insecurity. The localized manifestation of these dimensions helps clarify how transportation access influences food insecurity—particularly through missed grocery trips. For example, limited availability and affordability of transportation services can significantly disrupt food access for vulnerable individuals.
Previous research has identified certain behavioral and socio-economic factors as predictors of food insecurity, including depression and reliance on public assistance [18], as well as factors associated with English proficiency, socioeconomic status, and loneliness [19]. These studies have used surveys to investigate how individual and neighborhood-level factors [20] influence food insecurity. Another line of research primarily uses GIS-based measures to assess the relationship between the proximity of population groups to community features such as infrastructure and food access [21,22,23]. Despite these contributions, these studies do not inform on an individual’s ability to navigate community resources to access food, leaving a gap in understanding how transportation access impacts food security. Manikas et al. [15] identified mobility factors, such as distance to roads and time to access food, which directly influence food insecurity. We build on this research, examining how non-geographic factors, such as individual and community barriers to transportation access, influence food insecurity.
This study contributes to the growing body of literature on food insecurity and transportation by focusing on food access barriers among TD populations using a large city in the southwest USA as the site of inquiry. Our research answers two crucial questions: (1) How do transportation availability and affordability affect food security among TD populations in economically disadvantaged communities? (2) What role do community-level factors, such as poverty rates and walkability, play in influencing missed grocery trips among transportation-disadvantaged populations? Using survey data from 414 participants, we examined how transportation availability and affordability influenced missed grocery trips. Additionally, we investigated the role of neighborhood-level factors, such as walkability and poverty rates, in shaping food access for these vulnerable populations. By addressing these research questions, this study aimed to provide a deeper understanding of the transportation barriers that TD populations face in accessing food and offer insights into potential interventions for policymakers and community planners.

1.1. Theoretical Orientation and Predictors of Food Insecurity

This study used the theory of transportation justice as a lens to examine the relationship between transportation access and food insecurity. Vulnerable individuals with the compounded impact of socio-economic hardship face unequal response to transportation access [24]. Individuals experiencing these transportation disadvantages have limited options to access a personal vehicle or pay for other transportation options, resulting in negative physical and mental health impacts [25]. Beyazit [26] argues the relationship between transportation access and economic and social participation. In this context, transportation access is necessary for individuals to access the needs of basic daily living [4], such as nutritious food. The following outlines the predictors of food insecurity as they relate to this theory.

1.1.1. Background and Gaps in the Literature

Previous scholars found that behavioral and economic factors, such as depression and being on public assistance, led to a higher prevalence of food insecurity, especially among older adults [18]. Others build on this argument, finding that loneliness and social isolation exacerbate the condition of food insecurity [19]. Leroux et al. [27] also found regional disparities in the locations of individuals experiencing food insecurity, exacerbated among lower-income Hispanic and African American individuals [28]. While these studies highlight disparities in food access based on income and race, and go on to identify neighborhood-level factors [20], they overlook how mobility and transportation barriers impact an individual’s ability to access grocery stores and purchase food independently. Petroka et al. [5] argued that it is not just about the affordability and availability of food but also about accessing the food and overcoming the challenges associated with limited transportation options.
While previous studies used GIS-based measures of food access, such as distance and proximity to grocery stores, to understand food insecurity [21,22,23,29], we argue they do not adequately address the nuanced, non-geographic and individual barriers to food, such as the ability of the TD population to access and afford transportation services. These studies also do not account for the safety and security of individuals navigating the infrastructure, such as sidewalks and crosswalks that facilitate access to public transit stops [30]. Indeed, Hunt et al. [19] found that neighborhood factors, such as social isolation and neighborhood cohesion, matter in understanding food access, yet they do not demonstrate that transportation access barriers enhance this risk. We argue that transportation plays a key role in understanding an individual’s ability to navigate their environment to access healthy and nutritious food. Such an understanding is nuanced by the community’s lack of adequate transportation services, the inability of lower-income individuals to afford services, and the poor infrastructure and safety issues surrounding access to services. This consideration is necessary because TD individuals may rely heavily on, for instance, the availability of public transportation or community-based transportation services to make trips to the grocery store. Supporting this argument, Caspi et al. [22] found that individuals who perceived they could not access food in their community due to a lack of reliable transportation were associated with reported food insecurity; however, these dimensions of transportation remained unexamined.
While online ordering and grocery delivery services aim to make food access more equitable, especially following the COVID-19 pandemic, significant concerns remain regarding upcharges on items compared with purchasing in-store, as well as the lack of in-store coupons or discounts applied to online purchases and the perceived inability to control food selection [31]. These financial barriers, associated with the costly delivery, are compounded by personal financial constraints due to limited income. Therefore, we argue that a more comprehensive understanding of food insecurity is needed that includes the role of transportation through the lens of availability, affordability, and accessibility for the individual. Following this framework, the following section outlines the predictors influencing food insecurity among the TD population.

1.1.2. Availability as a Transportation Determinant of Food Insecurity

While driving remains the preferred method of transportation for TD groups [32], challenges like poor weather, vision limitations, and traffic congestion often limit their ability to drive [33]. For instance, car repairs or accidents can make older people dependent on family and friends for rides, leading to missed grocery trips if family and friends are inconsistent [32]. Among older adults, driving personal vehicles is the preferred transportation method [34]. Schwartz et al. [35] found that individuals with a disability expressed greater control of their shopping experiences when they could use personalized transportation. Therefore, we expect that individuals in the TD population with personal vehicles are less likely to miss grocery store trips than those who do not.
For individuals with a disability, transportation availability is also about the overall usability of the travel mode, such as having someone who can travel with you [35]. Community-based transportation (CBT) ride service alternatives provide ambulatory TD individuals with more ride service and mobility options, including a ride for a caregiver, and are known to improve their quality of life [11] by enhancing opportunities for physical activities and social engagement [36]. CBT ride services can include senior center transportation; veterans transportation; non-emergency medical rides; dial-a-ride services; carpooling programs; and contracted services from faith-based organizations, mission-driven non-profits, private vendors, and transportation network companies (TNCs) [37]. Further, online grocery store delivery is not viable for those without Internet access or support systems [38]. Given these findings, we anticipate that individuals using alternative ride services provided by CBT ride services are less likely to miss grocery store trips.

1.1.3. Accessibility as a Transportation Determinant of Food Insecurity

For TD populations in areas without nearby grocery stores, longer trips to access food are often necessary. Public transit can be a backup option when private transport is unavailable, but issues like unreliable service, unsheltered stops, and bag limits make it impractical. Also, fixed routes designed for commuting do not address the mobility needs of the TD population [11]. Baek [39] found that greater access to public transportation did not reduce food insecurity, particularly among African American households and the impoverished. Thus, proximity to bus stops does not significantly reduce missed grocery store trips. Public transit services like paratransit are also found to have tradeoffs to availability, such as long wait times and inflexible booking options [35].
Living alone increases the TD population’s food insecurity risk [40]. The pandemic changed how individuals obtain food [41], and delivery services now offer a reliable solution for those living alone. However, those without the Internet or family assistance to help with online ordering may struggle to use these services. This reliance on family members for grocery shopping is more substantial for TD individuals living alone [38]. Changes in caregiver support or isolation may lead to gaps in food access [42], particularly when transportation alternatives are unavailable or unaffordable.

1.1.4. Affordability as a Transportation Determinant of Food Insecurity

The affordability of transportation matters in shaping access to basic daily living needs for vulnerable populations, resulting in health consequences. For instance, living in economically unstable areas without transportation worsens health outcomes as TD populations cannot access grocery stores or food pantries [27,43]. This financial vulnerability directly relates to missed grocery store trips and extends to other access options such as online shopping and home delivery. While online delivery offers an alternative to food access, the high costs of delivery strain personal finances [9,18]. The TD population’s food insecurity issue is exacerbated by financial constraints imposed by transportation affordability, making individuals with lower incomes more likely to miss grocery store trips than individuals with higher economic stability. Calloway et al. [14] introduced an important measure of utilization barriers, such as having limited knowledge, skills, or impairments that prevent the safe storage and use of food, to understand food insecurity. While not directly measured in our survey, this issue is relevant for vulnerable individuals. Indeed, for those financially vulnerable and primarily living alone, our argument of the relationship between transportation affordability and missed grocery store trips indirectly relates to this pillar, as individuals may be making financial tradeoffs, such as purchasing perishable food or paying utility bills to operate a refrigerator, deserving of additional study.
TD populations are often faced with elevated costs for transportation services. Scholars find that older adults with a disability face higher costs for transportation services due to increased costs related to caregiver support [44]. Mitra et al. [45] found that living alone and experiencing greater levels of disability compound this cost for the TD population and further limit mobility options. For some individuals, this results in making decisions to forgo trips that they essentially would have taken if costs were not so high [46], making the affordability of transportation matter in the context of grocery store access.
Due to these constraints, informal networks like friends and family are critical to the TD population’s transportation access. Scholars find that families become the de facto transportation service due to the high costs associated with transportation services that accommodate wheelchair users. These authors go on to find that while paratransit is an option to increase access, the long wait times and scheduling challenges create barriers, and local taxi options at USD 40 per ride are cost-prohibitive [47]. Lewis et al. [48] posit that transportation equity comes down to fair service cost, and ultimately, when costs are high, TD populations are negatively impacted by the financial burden [49]. For example, in a study of Uber Wav (Wheelchair accessible vehicles), Uber subsidized the cost of each trip for riders to reduce the overall cost, but the cost was still on average USD 30 per trip, and due to limited supply, riders experienced long wait times [50].

2. Materials and Methods

2.1. Study Design

This study design is a mixed-methods research initiative that is part of a broader project seeking to analyze community needs and institutional readiness for enhancing the coordination of on-demand ride services in a city in the southwest USA. This manuscript draws on a quantitative survey component of the larger project administered through non-profit on-demand ride providers in this city to better understand the nuanced needs of TD populations in the region. An Institutional Review Board (IRB) (Human Subjects Application-IRB-23-83) approved quantitative study design was conducted to explore (among other attributes) the association between transportation access and food security.

2.1.1. Target Population and Sampling Strategy

The target population and sampling strategy were based on data collected from the city of study, which had a population of 1.4 million, of which over 5% were TD. We used a purposive sampling approach in collaboration with the nonprofits and service providers to reach a wide demographic in the community. Efforts were made to recruit both online and offline participants to overcome access barriers and bias resulting from the lack of Internet access among this population.

2.1.2. Survey Administration

The survey administration was restricted to individuals aged 65 years and older and those aged 18 and older with a disability. The survey was conducted online and offline using the same survey questionnaire anonymously, with written informed consent. An online survey was distributed to the target population via flyers, emails, and social media in collaboration with local nonprofits and transportation service providers. To overcome sampling bias that may have excluded anyone without Internet access, we hosted eight focus groups at senior centers and a nonprofit center for individuals with disabilities to assist in completing the survey. The survey was professionally translated into Spanish. Flyers in English and Spanish were distributed by various nonprofit agencies, through city websites, and via emails to nonprofit clients, and promoted on social media platforms of different government and nonprofit agencies in the community.

2.1.3. Instruments and Measures

The survey instrument assessed specific measures of transportation habits and needs across various transportation options, including public transportation, paratransit, community-based transportation (CBT) ride services, and personal vehicles. It explored travel preferences and service levels from curb-to-curb to door-through-door and incorporated demographic and socioeconomic data. Additionally, it evaluated access to technology, such as smartphones, and examined how living arrangements affected transportation challenges.

2.1.4. Sample Characteristics and Data Cleaning

The survey received responses from 538 participants. Among the survey responses collected, responses without geographical location information or with more than 10 missing values on questions were excluded from the dataset. The final sample size was 414. The final dataset included various types of TD populations, including individuals with disabilities with ages over 18 (18.9%) and individuals with ages over 65 (41.8%) and sex (male = 31.8%). Our research was not nuanced by race, due to the weak correlation level of racial features. Although the racial demographic of survey respondents represented the regional demographic features well, the correlation results showed that racial features were not significantly related to grocery trips. The income level was also excluded from the demographic features due to its low response rate. Instead, the regional poverty level was included as a proxy for income level. Poverty level, measured by the poverty income ratio, was not just an indicator of individual households’ income level but also an indicator of the transportation infrastructure of the community [51,52].

2.2. Measurements

The dependent variable—missed grocery store trips—was a proxy for food insecurity, consistent with prior research [30]. Drawing on the theoretical framework surrounding the determinants of food insecurity, this condition was multifaceted, shaped by access to food, economic stability, social and health-related factors, and transportation availability. Given that missed grocery trips may stem from challenges related to both the availability and affordability of transportation, this study examined three distinct measures of missed trips: (1) any missed grocery trips, (2) trips missed due to transportation unavailability, and (3) trips missed due to transportation unaffordability. For each dependent variable, a response indicating a missed grocery trip was coded as “1”, while responses indicating no missed trips were coded as “0”. The full coding scheme used in this analysis is presented in Table 1.
The independent variables in this study captured five broad dimensions: mobility, access to services, economic stability, community characteristics, and demographic factors. Variables related to socioeconomic status, demographics, health status, age, and income were adapted from Leroux et al. [27] while measures related to transportation disadvantage (TD) status and general health condition, as indicators of mobility, were drawn from Hunt et al. [19]. A binary flag for individuals aged 65 and older was also included.
The TD status variable was categorical, with values coded as follows: 1 for “older adult”, 2 for “person with a disability”, and 3 for “older adult with a disability”. The “older adult with a disability” group served as the reference category in the analysis. The age 65+ indicator was coded as 1 for “yes” and 0 for “no”, putting individuals whose age was under 65 in the reference group. Self-reported general health was measured on a five-point ordinal scale, ranging from 1 (“poor”) to 5 (“excellent”).
Following discussions in Loukaitou-Sideris et al. [4], this study also included indicators of access to services, including the distance to the nearest grocery store, transit stop, and senior center, as well as car ownership and use of ride services.
Grocery store locations were sourced from ArcGIS Business Analyst, while senior centers and bus stops were obtained from the city’s databases. Car ownership and ride service usage were coded as 1 for “yes” and 0 for “no”. To explore the gendered implications of food insecurity, we created flags for respondents who identified as male or female. On the other hand, homeownership status served as an indicator of economic stability. The homeownership variable was coded as 1 for “homeowner” and 0 for “living in rental housing”. Finally, the tract variable, poverty income ratio, was a proxy for community features. As an indicator of individual income and community transit infrastructure, the poverty income ratio was measured by the percentage of individuals below the poverty line in each census tract, sourced from US Census data.

2.3. Data Analysis Plan

This study is built on previous research examining food insecurity in specific populations [19,27], demonstrating the value of logistic regression when using a binary dependent variable to identify measures and the probability of risk for food insecurity. Logistic regression analysis was performed using SPSS 29.0 to evaluate the impact of mobility conditions, access to services, economic stability, community characteristics, and demographic factors on missed grocery trips. After excluding observations without geographical location information and those with more than 60% missing values, the final sample had missing values ranging from 0% to 57%. All observations in the final sample were retained. With this dataset, the listwise deletion method was applied for logistic regression analysis, resulting in a logistic regression analysis of 218 to 229 cases from a total sample size of 414 participants.
The basic regression equation for these models can be expressed as
ln [p/(1 − p)] = α + β1X1 + β2X2 + β3X3 +… βtXt + e
where p is the probability of missing a grocery store trip; ln[p/(1 − p)] is the log odds ratio or logit; α is the intercept; X1, X2, and X3 represent the control variables; and e is the error term.

3. Results

3.1. Descriptive Results

The descriptive statistics are presented in Table 2. The primary outcome of missed grocery trips was examined across three models: missed trips for any reason, transportation unavailability, and transportation unaffordability. Overall, 29.9% of participants reported missing grocery trips for any reason, including transportation barriers. Notably, 25.4% of participants missed grocery trips due to transportation unavailability, while 18.4% attributed missed trips to unaffordability.
Regarding mobility, self-reported health was measured on a 1–5 scale, with a mean score of 2.73 (SD = 1.098), indicating that participants predominantly fell into the “poor” to “moderate” health range. The TD population was categorized into three groups: older adults (41.8%), individuals with disabilities (18.9%), and those with both conditions (39.3%), with the latter serving as the reference group in the analysis.
The analysis of service access showed that participants lived an average of 0.65 miles (SD = 0.675) from the nearest grocery store, although some had to travel up to 8 miles. The average distance to transit stops was 0.403 miles (SD = 0.898), with some participants located as far as 8 miles away. For senior centers, the average distance was 2.64 miles, with 10% of respondents living beyond the 5-mile service radius, indicating limited access for some. In terms of transportation, 46.6% owned a vehicle, providing independent mobility, while 56.2% relied on ride services.
Indicators of economic stability showed that 58.8% of respondents reported homeownership, with 41.2% living in rental housing. The federal poverty rate in these neighborhoods averaged 19.14% (SD = 11.56), reflecting many residents’ economic hardships. The sample included 31.8% males, and 62.4% were aged 65 or older. The average median income of our sample was USD 47,500.

3.2. Missed Grocery Trip for All Reasons

Table 3 presents the logistic regression models predicting the likelihood of the TD population missing grocery trips, considering transportation accessibility and affordability. In these models, a positive coefficient (β) and an Exp(β) value greater than 1 indicated a higher likelihood of missing grocery trips as the corresponding variable increased, while a negative β and an Exp(β) value less than 1 suggested a lower likelihood. The chi-square score identified the significance of the model. According to the chi-square scores in the results, all three models were statistically significant. Nagelkerke R-squared, which indicated the explanatory power of the model, ranged from 0.358 to 0.417.
Model 1 examined “missed grocery trips for all reasons”. General health conditions, homeownership, ride service use, and neighborhood poverty rates were significant predictors. Individuals in better health were less likely to miss grocery trips due to ride affordability, with an odds ratio (OR) of 0.568, B = −0.565, indicating that those in poorer health (poor or fair conditions) had a 60.8% chance of missing trips for any reason. On the other hand, individuals over 65 were less likely to miss the grocery trip than those younger than 65 (OR = 0.386, B = −0.952). Individuals older than 65 had a 27.8% chance of missing trips for any reason. In addition, individuals who were old (age over 65) and had disabilities were more likely to miss their trip. The results showed that individuals who were old and had a disability had a 78.5% likelihood of missing the trip for any reason, with an odds ratio (OR) of 0.261, B = −1.345 for disability group. These results showed that age itself did not negatively impact making trips. Health conditions and disability were significantly evident.
Homeowners were less likely to miss grocery trips than renters (OR = 0.147, B = −1.917), highlighting that renters had a 78.5% likelihood of missing a grocery store trip for any reason. Additionally, ride services were a strong predictor, with users having a much higher probability of missing trips (OR = 2.491, B = 0.913). Individuals relying on ride services had a 71.4% chance of missing grocery trips; these individuals were about three times more likely to miss trips than those who did not. Individuals with a car were less likely to miss grocery trips with an OR of 0.36, B = −1.022, indicating that those who did not have a car had a 68% chance of missing trips for any reason.

3.3. Missed Grocery Trip Due to Transportation Availability

Model 2 examined missed grocery trips due to transportation availability, indicating that individuals with better health and greater walking capacity were less likely to miss trips. Consistent with model 1, general health conditions significantly affected missed trips, with healthier individuals exhibiting a reduced likelihood of missing a grocery trip due to the unavailability of transportation (OR = 0.445, B = −0.809). These findings suggest that individuals with poor health or limited mobility are more likely to miss grocery trips. On the other hand, individuals over 65 were less likely to miss the grocery trip than those younger than 65 (OR = 0.277, B = −1.283). Individuals older than 65 had a 21.7% chance of missing trips due to transportation availability issues. The interaction between age 65 and car ownership provided a greater advantage for these individuals, who did not miss grocery store trips.
Participants relying on ride services were more likely to miss trips due to transportation availability (OR = 2.797, B = 1.029), with a 73.7% likelihood of missing trips. Additionally, disability status played a significant role; individuals categorized as both “old and disabled” were more likely to miss grocery trips compared with those with disabilities (OR = 0.185, B = −1.688), with an 81.5% probability of missing trips. The results in model 2 also confirmed that being 65 itself did not make an individual TD. These individuals may have relied more on family and neighbors for help and used online ordering. Health conditions and disability were more critical to their travel behaviors.
Homeownership emerged as a protective factor, with homeowners significantly less likely to miss trips due to transportation availability than renters (OR = 0.146, B = −1.924), implying that renters faced an 87.3% probability of missing essential trips. Community poverty levels also significantly impacted transportation availability.

3.4. Missed Grocery Trip Due to Transportation Affordability

In model 3, several factors significantly influenced the likelihood of missing grocery trips. The model showed that individuals with better health were less likely to miss grocery trips due to transportation costs (OR = 0.620, B = −0.479), with those in poorer (poor and fair conditions) health having a 65.9% probability of missing grocery store trips because of affordability issues.
Car ownership also played a critical role. Individuals who owned a car were significantly less likely to miss grocery trips due to transportation costs than non-owners (OR = 0.24, B = −1.429), with non-car owners facing an 81% likelihood of missing trips due to the cost of transportation. Sex was another determinant in this model, with males more likely to miss grocery trips due to affordability than females (OR = 2.856, B = 1.049), implying a 74.1% probability of missed trips among male participants due to transportation costs. Being 65 and older was insignificant in model 3 as an individual variable. Being 65 and older and relying on ride services could reduce challenges for older adults, according to the interaction between age over 65 and ride service usage.
Homeownership served as a proxy for economic stability, with homeowners being less likely to miss grocery trips due to transportation affordability issues than renters (OR = 0.137, B = −1.986). Renters, by contrast, had an 87.9% probability of missing essential trips due to the cost of transportation. The poverty level within a community significantly impacts the likelihood of missed grocery trips. Participants living in areas with a higher poverty rate were more likely to miss grocery trips due to transportation affordability issues (OR = 1.038, B = 0.037), with a 3.8% increase in the probability of missing trips for each 1% increase in the community’s poverty rate.

4. Discussion

This study showed that TD populations face food insecurity due to missed trips to grocery stores. We addressed the underexplored role of transportation through availability, accessibility, and affordability as a proxy for missed grocery store trips [30]. Traditional food insecurity research [18,19,28] focused on behavioral, social, economic, and demographic predictors of food insecurity. More importantly, this research provides evidence of underexplored aspects of the nuanced, non-geographic, and individual barriers to food. All three models showed consistent predictors of grocery store trips, including self-reported health; reliance on community-based transit; lack of a personal vehicle; and economic instability, such as renter status or living in areas of high poverty. Together, these findings suggest that transportation-disadvantaged individuals face challenges of being both economically and physically constrained. We found that being 65 years or older was not, by itself, a strong predictor of missed trips, but when considered in the context of disability and reliance on ride services, the risks for missed grocery store trips were greater.
While GIS-based research on food insecurity identified food insecurity in proximity to community features [21,23], our model demonstrated that transportation access variables were crucial predictors of food insecurity. Limited access to rides and reliance on community-based transportation directly impacted TD food insecurity. Indeed, the lack of affordable rides and personal vehicles further exacerbated these issues. Our findings show that the personal car remains the great equalizer, given that individuals with a car were less likely to miss trips.
Our results on the challenges with ride services align with previous research, which finds that community-based transportation services cannot meet demand and lack the flexibility to meet riders’ real-time travel needs [22]. When individuals rely on ride services, they face challenges in meeting their food security needs due to deeper issues associated with accessibility and flexibility, as suggested by Petroka et al. [5]. Community-based nonprofit ride services are typically limited to weekdays, require long lead times for reservations, and, due to demand, limit rides to medical trips over grocery store trips [4]. Those who rely on ride services may not have other transportation options. While Bezerra et al. [53] argue that food access is hindered due to the failure of interlinked systems and policies to support vulnerable individuals, our research operationalizes one of these failures as the inadequacy of public transit infrastructure and the limited capacity of ride services to fill the void in supply. This finding highlights the need for policy reforms to address the current providers’ lack of capacity and flexibility, and policies to incentivize the growth of private providers in the community.
Those in general good health are less likely to miss trips, which makes them good candidates to ride fixed-route services, leaving CBT ride services for those who face personal mobility challenges. While only an indirect relationship, we argue that this finding bridges Calloway et al.’s [14] utilization barrier as a pillar of food insecurity, suggesting a possible argument that if those in poor health have difficulty storing food and can only shop for it intermittently, a lack of transportation options exacerbates their challenges of food insecurity.
Further, previous research has demonstrated that TD experience systematic barriers to transportation access due to variations in travel time (ease of travel) to reach desired destinations [54] or limited trip availability with different service providers [21]. Building on this, we provide empirical evidence that vulnerable subgroups have disproportionate access to food in their community. Individuals over 65 with disabilities were less likely to complete grocery store trips due to limited transportation services. Our results indicated that the average neighborhood distance of respondents to the grocery store was 0.65 miles, but nearly 30% of respondents missed grocery store trips. Even with respondents living close to grocery stores, the sidewalk conditions were often inappropriate and unsafe, making it difficult for them to access the grocery store. Better proximity did not automatically mean better food access. We learned from the area’s nonprofit providers that participants experienced extreme difficulties walking to the grocery store due to the poor sidewalk conditions and safety concerns in their neighborhoods. This underscores the need for attention to local policy interventions that fund upgrades to the local sidewalk infrastructure, allowing residents to safely walk to the grocery store and maintain their health and independence. Transportation access directly impacts food security.
Adding nuance to this debate, Kansanga et al. [12] challenged income-based theories of food insecurity; yet, we found that renters and those living in poverty had increased risks for food insecurity when grocery store trips were limited due to transportation affordability issues. Renters may be facing tradeoffs between paying rent and affording transportation options. Shim et al. [43] also found that living in economically unstable neighborhoods correlated with negative health outcomes due to limited access to nutritious foods. Our research reinforced this, showing that a lack of transportation exacerbated the issue. To this point, we have also found that housing stability is an important aspect of understanding transportation access and food insecurity.
Our findings reinforce the broader findings in the food insecurity literature that affordability creates a barrier to food access [14,15]. Respondents without access to a personal vehicle were more likely to miss trips due to affordability, highlighting the need for policies that support subsidies, such as transportation vouchers, to enhance the independence and decision-making of riders, or programs like weekly shuttles between areas with high concentrations of TD populations and grocery stores. In addition to improving transportation access, there is value in expanding services like Meals-on-Wheels and including the delivery of subsidized groceries to improve choice and variety. This model could significantly enhance food access for TD individuals who are unable to shop independently, especially those in poor health or with limited mobility. However, for nonprofit organizations to reliably implement such services, consistent programmatic support at the local level is essential. This underscores the need for active engagement from municipal and county structures, such as senior services departments and area agencies on aging. These agencies must go beyond traditional care coordination and intentionally invest in initiatives that improve food access for TD populations by addressing mobility and service delivery gaps at the community level.
These findings support arguments for advocating transportation justice for our most vulnerable populations through targeted transportation interventions.
This study has some limitations that restrict its generalizability. The sample of TD populations was limited due to a lower response rate than anticipated. The model also captured self-reported travel behavior that may be limited due to incomplete frequency or trip purpose data. Future research may benefit from participants using a travel app to collect real-time travel data. Furthermore, since mental and physical abilities typically decline with age, people often adjust their behavior accordingly. Future research should investigate how older adults adapt their behaviors to navigate their communities and access transportation as they encounter both physical decline and changes in their economic status. The respondents for this study were predominantly Hispanic, at over 60 percent. Due to the limited variation, the models did not assess whether the association between transportation and food insecurity varied across racial groups. Future studies should consider administering the survey at a site with greater racial and ethnic diversity.

5. Conclusions

Understanding inequalities in food access matters because place-based factors influence an individual’s social support [55], limiting access to the essentials of daily living. Following the work of Balachandran [55], a quantitative survey of the experience of an individual’s living situation and the likelihood of missed trips highlights how public and nonprofit sector involvement (or lack thereof) impacts vulnerable people. This research advances current methodological approaches by utilizing a survey that directly addresses the transportation and mobility challenges of the TD population, shedding light on the relationship between geographic and non-geographic patterns of infrastructure and travel choices, as well as the well-being of the TD population.

Author Contributions

Conceptualization, L.M.K. and S.B.; methodology, J.K.; software, J.K.; validation, L.M.K., J.K., S.B. and S.K.; formal analysis, J.K., L.M.K. and S.B.; investigation: L.M.K., S.B., S.K. and J.K.; data curation, J.K.; writing—L.M.K. and J.K.; writing—review and editing, S.B., S.K., J.K. and S.A.; visualization, J.K.; supervision, L.M.K.; project administration, L.M.K. and S.A.; funding acquisition, L.M.K. and S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Ride Connect Texas, San Antonio, TX, GF40308.

Institutional Review Board Statement

This study was approved by the Institutional Review Board of the University of North Texas, IRB-23-83, approved 12 April 2023.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support this study’s findings are available from Ride Connect Texas, located in San Antonio, Texas. However, restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the authors upon reasonable request and with the permission of Ride Connect Texas.

Acknowledgments

We would like to thank Ride Connect Texas and the San Antonio Area Foundation for their participation and support of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Variables and coding scheme.
Table 1. Variables and coding scheme.
VariablesValue/Code
DVMissed grocery trips for availability1 = yes, 0 = never missed
Missed grocery trips for affordability1 = yes, 0 = never missed
Missed grocery trips for all reasons1 = yes, 0 = never missed
Access to
services		Nearest grocery distance (mi)Mile, distance to nearest grocery store
Nearest senior center distance (mi)Mile, distance to nearest senior center
Nearest transit stop distance (mi)Mile, distance to nearest transit stop
Car ownership 1 = yes, 0 = no/ref: no car
Ride service use1 = yes, 0 = no/ref: do not use ride service
MobilityGeneral health conditionPoor (1), fair (2), good (3), very good (4), excellent (5)
Age over 651 = yes, 0 = no/ref: under 65
TD dummy (ref: old and disability)Old (1), disability (2), old and disability (3)
TD dummy (old)-
TD dummy (disability)-
DemographicSex1 = male, 0 = female/ref: female
Economic
Stability		Home ownership1 = own home, 0 = rent/ref: rent
Community FeaturesPoverty income ratio% of individuals below the poverty line in each census tract
Table 2. Descriptive statistics of variables in the logistic regression model.
Table 2. Descriptive statistics of variables in the logistic regression model.
VariableResponseFreq (x = 1)MeanSDMinMax
Missed grocery trips for availability27225.40%0.250.43601
Missed grocery trips for affordability28818.40%0.180.38801
Missed grocery trips for all reasons27429.90%0.30.45901
Nearest grocery distance (mi)414-0.650.6750.0447.628
Nearest senior center distance (mi)414-2.6442.1440.11115.78
Nearest transit stop distance (mi)414-0.4030.8980.0068.849
Car ownership30546.60%0.470.501
Ride service use30656.20%0.560.49701
General health condition271-2.731.09815
Age over 6530662.40%0.620.48501
TD dummy (ref: old and disability)------
TD dummy (old)37141.80%0.4180.49301
TD dummy (disability)37118.90%0.1890.39101
Sex30231.80%0.320.46601
Home ownership26058.80%0.590.49301
Poverty income ratio414-19.14211.5620.555.1
Table 3. Key determinants of food insecurity contributing to the unavailability of rides, unaffordability of rides, or both.
Table 3. Key determinants of food insecurity contributing to the unavailability of rides, unaffordability of rides, or both.
Model 1Model 2Model 3
All ReasonsTransportation
Availability		Transportation
Affordability
VariableBOdd RatioBOdd RatioBOdd Ratio
Nearest grocery distance0.081 (0.316)1.0850.269 (0.299)1.309−0.019 (0.378)0.981
Nearest senior center distance0.058 (0.120)1.060.034 (0.125)1.0340.118 (0.141)1.125
Nearest transit stop distance0.022 (0.277)1.0230.107 (0.287)1.1130.109 (0.315)1.115
Car ownership−0.755 (0.441) *0.47−0.526 (0.473)0.591−1.429 (0.594) **0.24
Ride service use0.913 (0.412) **2.4911.029 (0.454) **2.7970.726 (0.497)2.067
General health condition−0.565 (0.195) ***0.568−0.809 (0.226) ***0.445−0.479 (0.234) **0.62
Age over 65−0.952 (0.508) *0.386−1.283 (0.538) **0.277−0.024 (0.613)0.976
TD dummy
(old and disability)
TD dummy (old)−0.747 (0.492)0.474−0.868 (0.547)0.420.207 (0.579)1.23
TD dummy (disability)−1.345 (0.613) **0.261−1.688 (0.649) ***0.185−0.089 (0.678)0.915
Sex0.359 (0.405) *1.4310.335 (0.428)1.3991.049 (0.472) **2.856
Home ownership−1.917 (0.433) ***0.147−1.924 (0.469) ***0.146−1.986 (0.540) ***0.137
Poverty income ratio0.025 (0.017)1.0260.024 (0.018)1.0250.037 (0.019) *1.038
Constant1.300 (0.898)3.6681.646 (0.947) *5.187−1.264 (1.064)0.283
Sample size (N)219218229
−2 Log likelihood180.945160.801140.236
Chi-square70.180 ***69.032 ***52.147 ***
Nagelkerke R20.4020.4170.358
Note: *** α < 0.01, ** α < 0.05, * α < 0.10.
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MDPI and ACS Style

Keyes, L.M.; Kim, J.; Balachandran, S.; Kuttler, S.; Andrew, S. Missing Meals and Missed Rides: Transportation Barriers to Food Access for Vulnerable Populations. Urban Sci. 2025, 9, 198. https://doi.org/10.3390/urbansci9060198

AMA Style

Keyes LM, Kim J, Balachandran S, Kuttler S, Andrew S. Missing Meals and Missed Rides: Transportation Barriers to Food Access for Vulnerable Populations. Urban Science. 2025; 9(6):198. https://doi.org/10.3390/urbansci9060198

Chicago/Turabian Style

Keyes, Laura M., Jintak Kim, Sowmya Balachandran, Sara Kuttler, and Simon Andrew. 2025. "Missing Meals and Missed Rides: Transportation Barriers to Food Access for Vulnerable Populations" Urban Science 9, no. 6: 198. https://doi.org/10.3390/urbansci9060198

APA Style

Keyes, L. M., Kim, J., Balachandran, S., Kuttler, S., & Andrew, S. (2025). Missing Meals and Missed Rides: Transportation Barriers to Food Access for Vulnerable Populations. Urban Science, 9(6), 198. https://doi.org/10.3390/urbansci9060198

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