Public Transit Travel Research Articles

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

Exploring the spatial relationship between urban built environment and green travel: An improved semi-parametric GWR approach

Public transit and non-motorized travel offer numerous environmental, social and public health benefits. Existing literature focuses on the correlation between the built environment and green travel in metropolitan centers, with limited studies on suburban new towns, essential for comprehensive urban expansion and sustainable development. This paper examines the effect of the built environment on public transit and non-motorized travel by using point of interest data and household economic data from Jiading District, Shanghai. An innovative framework is introduced, based on the geographically weighted regression model, which captures spatial heterogeneity while balancing the inequality and directionality of different areas in the transportation network. The results demonstrate that the proposed improved semi-parametric geographically weighted regression outperforms the traditional geographically weighted regression model in evaluation capabilities. A distinct spatial heterogeneity in travel patterns is identified between central and peripheral areas. The densely populated central zones exhibit a preference for intra-district travel, often opting for non-motorized modes due to their convenience and health benefits while peripheral areas serve as crucial connectors, facilitating long-distance travel within the metropolitan area through public transit and multimodal transportation. Regional transportation infrastructure construction and socioeconomic factors also affect the choice of green travel. The findings could provide theoretical references and policy implications to the development of green transportation and urban sustainability.

Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework

During a pandemic or natural disaster, people may alter transit usage behavior due to perception of changes in the environment. To effectively respond to these crises, it is important for governments and public transit agencies to understand when these changes occurred and how they were affected by relevant policies and responsive strategies. In this study, we develop a methodological framework based on Bayesian online changepoint detection (BOCD) to identify the occurrence time, direction, and persistency of changes in individual-level transit usage. We demonstrate the effectiveness of this framework in informing government decision-making in the context of COVID-19. Using Jeju Island, South Korea as a case study, we apply the framework over a nearly two-year smart card dataset collected from the beginning of 2019 till nine months into the pandemic. By focusing on frequent transit users, we detect when these users significantly changed their transit usage frequency during the pandemic and identify several types of users who experienced different behavior change patterns. Besides demonstrating the great heterogeneity in individual-level behavior changes, we perform a regression analysis to further understand how these changes were affected by key government policies (e.g., Risk alert, Social distancing, Public transit policy, and Eased social distancing). Our results suggest that only certain sets of policies appear to have significant effects. In particular, introducing Risk alert would cause a 277% to 317% increase in the number of users who reduced transit usage frequency. Policies that eased social distancing, though, would cause a 134% to 155% increase in the number of users with travel frequency increase. The proposed BOCD framework enables a scalable solution to identifying and understanding changes of individual transit behavior. The methodology and findings are beneficial for developing targeted policies and interventions to facilitate daily travel and public transit operations during public health crises.

Identifying the Optimum Combination of Use of Smartphone Apps and Hedonic Motivation for Increasing Public Transit Loyalty: An fsQCA Approach

Intensive use of public transit plays an important role in commuting and daily travel in China and many other countries. With the widespread use of smartphones and other information and communication technologies, using mobile applications while on public transit has become increasingly popular, and more passengers rely on them during travel. To understand how the use of smartphone apps during transit travel and hedonic motivation can improve passengers’ willingness to use public transit, this study examines their combined effects. Smartphone apps used during transit travel can be classified into four categories: entertainment; information retrieval; e-ticketing; and mobile working. Drawing on complexity and configuration theories, we propose a theoretical model that is empirically tested using fuzzy set qualitative comparative analysis. Based on an empirical study of 567 Shanghai residents, we present eight different combinations of the use of smartphone apps during transit travel and hedonic motivation that explain a high degree of public transit loyalty. These can be categorized into three types: (a) mobile working oriented; (b) hedonic motivation oriented; and (c) digital management oriented. The findings suggest that satisfaction and happiness with public transit are sufficient but not necessary conditions for public transit loyalty. If passengers use smartphone apps or experience hedonic motivation during public transit travel, their satisfaction and happiness levels are likely to increase. Overall, this paper provides insights to guide policymakers and public transit operators in considering more external factors that might improve passengers’ travel satisfaction, happiness, and loyalty.

Pedestrian-oriented zoning moderates the relationship between racialized economic segregation and active travel to work, United States

ObjectivePedestrian-oriented zoning, including zoning code reforms (ZCR), may be especially beneficial to racially and economically segregated communities, which may lack built environment features that support physical activity. This study examined associations between racialized economic segregation, measured by quintiles of the Index of Concentration at the Extremes, and public transit (PTW) and active travel (ATW) to work, and whether associations were moderated by pedestrian-oriented zoning provisions and ZCR, respectively. MethodsZoning codes effective as of 2010 representing 3914 US municipalities (45.45% of US population) were evaluated for the presence of ZCR and eight pedestrian-oriented zoning provisions. These data were linked with American Community Survey 2013–2017 and NAVTEQ 2013 data on the outcomes and relevant covariates. Fractional logit regressions were computed with standard errors clustered on county. ResultsWorkers from more deprived quintiles were less likely to engage in PTW and ATW (OR = 0.22–0.55, p < 0.01), and tests revealed moderation by zoning (p < 0.05). ZCR was positively associated with PTW for the three most deprived quintiles (OR = 1.53–2.38, p < 0.01), and with ATW for the two most deprived quintiles (OR = 1.42–1.69, p < 0.01) and the second most privileged quintile (OR = 1.26, p < 0.05). In the most privileged quintile, the zoning scale score was negatively associated with PTW (OR = 0.91, p < 0.001) and ATW (OR = 0.94, p < 0.01). However, in the most deprived quintiles, the zoning scale score was positively associated with PTW (Q2: OR = 1.13, p < 0.01) and ATW (Q1-Q2: OR = 1.07–1.09, p < 0.05). ConclusionsPedestrian-oriented zoning can provide opportunities for ATW in the most deprived communities. Work is needed to explore zoning policy implementation in those communities.

Impacts of Connected and Automated Vehicles on Travel Demand and Emissions in California

The emerging connected and automated vehicle (CAV) technology is likely to bring significant changes to the transportation world. This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. We design a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050. These scenarios consider the ownership and operational type (e.g., private and shared) of CAVs, as well as additional policies such as pricing and the use of zero-emission vehicles (ZEVs) to curb potential impacts. The scenarios are: 0. Baseline (no automation); 1. Private CAV; 2. Private CAV + Pricing; 3. Private CAV + ZEV; 4. Shared CAV; 5. Shared CAV + Pricing; 6. Shared CAV + ZEV. Impacts of the introduction of CAVs across the transportation system are quantitatively estimated. The results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled (VMT) and emissions will likely increase, because of the relative convenience of CAVs. The results also show that VMT could be substantially affected by a modification in auto travel costs. This means that the implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help reduce tailpipe pollutant emissions in future scenarios.

Breaking down public transit travel time for more accurate transport equity policies: A trip component approach

In the Northern hemisphere, low-income transit users typically take shorter trips and use buses in a greater proportion, but there is limited knowledge on inequalities in the detailed characteristics of public transit trips.In this paper, we seek to understand and explain differences in segment components of transit trips across socio-economic groups. Trip components include access time, wait time, total travel time, overall trip speed and percent of trip time spent in access and wait (off-vehicle time).Using the Montreal metropolitan region travel survey (2013), we focus on the 46,186 transit trips made by 20,138 individuals for which household income was available. This data is augmented by time estimations of the aforementioned trip components, and combined with trips specific transit service measures calculated using General Transit Feed Specification (GTFS). We first assess the relationship between trip components and income using descriptive statistics. OLS regressions are then conducted to identify which individual and transport-based factors contribute to variations in trip component outcomes.Low-income transit users take more transit trips (especially bus and Metro) than their higher income counterparts and combine multiple transit modes through more frequent transfers. While average access and wait time are individually lower than those for trips of wealthier respondents, low-income total trip time is similar but trip speed is considerably lower. Lower-income populations also spend a greater share of their trips in access and wait time due to service and trip discrepancies including trip timing, proximity to metro stations and requirement for transfers. When controlling for other factors, the conditions under which trips by low-income individuals are taken mainly drive income differences.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea – a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators – from perspectives of trip frequency, spatial diversity, and travel range – to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors’ mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors’ transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

A mixed-methods investigation of older adults’ public transit use and travel satisfaction

Public transit may be a low-cost and environmentally friendly way for older adults to maintain independent mobility as they age, and yet little is known about older adults’ transit travel behaviours or travel satisfaction. To address this research gap, this paper explores older people’s (60+) responses to a travel survey on the Hamilton Street Railway (HSR). Distributed in 2019 to residents of Hamilton, Ontario, 5844 people responded to the survey, 1002 of which were aged 60 years or older. Descriptive statistics were conducted to assess older adults’ responses, as well as to compare these responses to respondents aged 18–59 years. A thematic analysis of responses to an open-ended question was also completed.The results indicate that older adults are more frequently occasional transit users (monthly, annually) than younger people who more frequently use the bus daily. Older adults are also more satisfied with the transit service than those aged 18–59 years. Older respondents were least satisfied with comfort amenities at bus stops and shelters and bus crowdedness. However, their satisfaction with these aspects of the service is not statistically significantly different from younger adults. From the thematic analysis, the top barrier shared by older adults is related to accessing the transit service. Across older adults, women, employed people, and those without a car or a driver’s license were more likely to be frequent transit riders. Taken together, this paper sheds light on an understudied population’s transit experiences and can be used to inform policy on how to make public transit more age-friendly.

The Value of Having a Public Transit Travel Choice

The value of having a public transit travel choice is occasionally acknowledged by planners but never quantified; this paper provides a methodology to quantify it. This value of having a public transit choice is in addition to public transit’s benefits to users and non-users as a result of the improved performance of other modes in the transportation system resulting from the public transit investment. The value of choice accrues to the total population that has access to public transit, not just those who chose to use it or those who benefit because others have chosen it. This paper develops a methodology and a crude but plausible estimate of the value of choice for public transit using data describing features of U.S. daily personal travel in 1995. For perspective, this estimate is compared with the total operating and capital expense of providing public transit in the United States. The result indicates that the value of choice alone is comparable in magnitude to the cost of providing public transit in this country.

Public transit travel choice in the post COVID-19 pandemic era: An application of the extended Theory of Planned behavior

It is widely reported that the COVID-19 pandemic has reduced ridership and brought severe challenges to urban public transit systems in many countries. The impact of the COVID-19 pandemic on individual people’s choice of public transit may continue for a while after the peak of the crisis. However, there is insufficient detailed knowledge of how individuals respond in the post-pandemic context and make choices on public transit travel. This paper contributes fresh evidence for this by looking at Beijing as a case. The theoretical framework of the Theory of Planned Behavior is used to model individuals’ public transit travel choice-making processes along with three additional constructs representing the impact of the pandemic and the nature of urban mobility behaviors, namely perceived knowledge of COVID-19, the psychological risks of COVID-19, and travel habits. Structural equation modeling is used in model estimation. We point out that there may be potential differences between the effects and meanings of model constructs in the post-pandemic context and in normal daily context. Interestingly, despite the higher psychological risk’s negative effects, higher perceived knowledge of COVID-19 has significantly positive effects on people’s decision-making processes. A strong pre-pandemic personal habit of traveling by public transit has significant and positive effects on post-pandemic intention and perceived behavioral control. Group comparisons show that “captive” transit users have higher psychological risk of COVID-19 than “choice” transit users, yet their transit use decisions are less influenced by it. Based on the modeling results, more behavioral experiments are needed to further inform efficient policy-making.

Comprehensive service quality evaluation of public transit based on extension cloud model

Prioritizing the development of public transit and enhancing its attractiveness is an important way to solve the problem of urban traffic congestion and achieve sustainable development. To improve the service quality and overall opera-tional efficiency of urban public transit, an evaluation index system related to the comprehensive experience of pas-sengers, service supply quality of public transit enterprises, and supervision of management departments was intro-duced from both the demand and the supply of public transit travel services. Based on the data distribution characteris-tics of the boxplot in statistics, the evaluation level and corresponding value range of each index were determined, and the comprehensive weight of the index was determined using the linear weighting method combining the analytic hierarchy process and the entropy weight method, so as to reduce the influence of single weighting method on the evaluation results of comprehensive service quality of public transit. An evaluation method of public transit compre-hensive service quality based on the extension cloud model was established. The evaluation results of the model were obtained by calculating the cloud affiliation and comprehensive certainty, and a reliability factor was used to test the evaluation results, which solved the problem of randomness and fuzziness in the process of comprehensive service quality evaluation of public transportation and made the evaluation results closer to the reality. Finally, the estab-lished comprehensive evaluation model was applied to a city for example analysis, and the corresponding evaluation level was obtained as good. The value of the reliability factor in the model was less than 0.01, indicating that the model has good applicability and a certain application value for the comprehensive service quality evaluation of public transit. The evaluation method fully considered a variety of evaluation indicators, specified the evaluation level of comprehensive service quality of public transit, and the evaluation results provide a theoretical basis for public transport enterprise to make targeted improvement measures.

Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models

Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.

Using Photovoice to Explore Teenagers’ Public Transit Experiences in a Suburban City

For many children, the transition to adolescence coincides with the expansion of everyday activities with teenagers taking up transit use to facilitate greater independent mobility and spatial exploration. This paper describes a photovoice exploration of public transit travel of 28 teenagers, 13 to 19 years of age, in Mississauga, Canada. The recorded experiences are grouped under four themes: access; safety; comfort and flexibility; and activity stacking. Findings may offer insights into local transport policies aimed at improving level of service for teenagers and other patrons including reduced group fares, design improvements, and onboard Wi-Fi connectivity.

Reliability in public transit networks considering backup itineraries

Itinerary planning serves an important purpose for travelers navigating through a public transit network. The uncertainty of travel times within the network must be considered since this uncertainty can lead to delays or missed transfers. If an initial transit service or a transfer between services is missed, then the traveler must have a contingency plan for reaching their destination. However, finding reliable itineraries with contingency plans is computationally challenging. This paper outlines an a priori policy that maximizes the reliability, i.e. the probability of reaching the destination within a travel time budget, while considering the most reliable primary itinerary and backup itineraries in case a transfer is missed.We develop a network search algorithm that finds the most reliable itinerary with backups, which accurately accounts for the uncertainty of travel times and frequency of alternative options in the recommendation. We compare our approach to state-of-the-art contingency research, Dynamic Journeying Under Uncertainty by Häme and Hakula (2013), and show that it more accurately calculates the reliability, is more intuitive to the traveler, and has a faster runtime. Additionally, we highlight the differences in reliability across times of day, travel time budgets, and city regions for a real-world transit network in Göttingen, Germany. This approach offers travelers an easy-to-understand tool for planning reliable public transit travel itineraries.

Exploring the Utility of Gini Coefficients as a Measure of Temporal Variation in Public Transit Travel Time

Travel times by public transit vary dramatically throughout the day. However, these variations are not the same for all commutes. If one commute origin and destination are connected by high frequency transit all day, then that commute will have less variation in travel time throughout the day. This research illustrates how that variation can be measured with gini coefficients so that inequality in travel times can be better understood. Using Denver as a case study, gini coefficients are calculated with 120 travel times by public transit throughout the day for each of over 200,000 origin destination pairs. The utility of the gini coefficient as a predictor of the percent of the population that drives to work is then explored using a regression. The regression results indicate that the gini variable has utility in predicting the percent of the population that drives to work.

Treating two pandemics for the price of one: Chronic and infectious disease impacts of the built and natural environment

Compact walkable environments with greenspace support physical activity and reduce the risk for depression and several obesity-related chronic diseases, including diabetes and heart disease. Recent evidence confirms that these chronic diseases increase the severity of COVID-19 infection and mortality risk. Conversely, denser transit supportive environments may increase risk of exposure to COVID-19 suggesting the potential for contrasting chronic versus infectious disease impacts of community design. A handful of recent studies have examined links between density and COVID-19 mortality rates reporting conflicting results. Population density has been used as a surrogate of urban form to capture the degree of walkability and public transit versus private vehicle travel demand. The current study employs a broader range of built environment features (density, design, and destination accessibility) and assesses how chronic disease mediates the relationship between built and natural environment and COVID-19 mortality. Negative and significant relationships are observed between built and natural environment features and COVID-19 mortality when accounting for the mediating effect of chronic disease. Findings underscore the importance of chronic disease when assessing relationships between COVID-19 mortality and community design. Based on a rigorous simulation-assisted random parameter path analysis framework, we further find that the relationships between COVID-19 mortality, obesity, and key correlates exhibit significant heterogeneity. Ignoring this heterogeneity in highly aggregate spatial data can lead to incorrect conclusions with regards to the relationship between built environment and COVID-19 transmission. Results presented here suggest that creating walkable environments with greenspace is associated with reduced risk of chronic disease and/or COVID-19 infection and mortality.

Quantifying the impact of weather on ride-hailing ridership: Evidence from Haikou, China

Existing studies have demonstrated that weather significantly affects public transit and active travel, but few studies quantify the impact of weather on ride-hailing ridership. Compared with other travel modes, the supply of ride-hailing can be flexibly adjusted following weather changes, thereby reducing the difficulty for users to hailing cars and improving vehicle utilization. This study quantifies the impact of weather on ride-hailing ridership in Haikou, China. Our main results include: (1) An increase of 1 mm in precipitation raises ride-hailing ridership by 0.39%, and an increase of 1 m/s in wind speed reduces ridership by 1.04%; (2) Wind speed and rainfall have stronger impacts on weekends than on weekdays. The increase of 1 mm in precipitation raises the ridership by 0.21% and 0.59% on weekdays and weekends, respectively; (3) The impact of weather on ride-hailing ridership is significant in the daytime while insignificant in the nighttime; (4) The effect of wind strengthens with travel distance, while the impacts of rainfall and air pollution diminish with travel distance; and (5) Trips starting from urban areas are more vulnerable to weather than those from suburban areas.

Revisiting the impacts of virtual mobility on travel behavior: An exploration of daily travel time expenditures

Abstract There has been a growing interest in the association between online activities and daily activity-travel patterns. An analysis of this relationship is even more crucial considering the major disruptions to out-of-home activity participation and travel due to the COVID-19 pandemic. This study contributes to the literature by exploring the relationships between Information and Communication Technology (ICT) use (focusing on telework and online shopping) and time spent traveling using different transportation modes. Using Tobit regression models, we investigate the impacts of ICT use on three travel alternatives: (1) automobile, (2) public transit, and (3) active travel. The results show that the effects of ICT use vary across these three travel modes. For example, all else being equal, respondents with higher durations of telework tend to spend less time on auto and transit. Respondents with higher durations of online shopping spend more time walking and bicycling. This study also explores whether the effects of ICT use on travel durations vary across groups with different socio-demographics and residential location characteristics. For instance, the study finds the greater the level of land-use mixture, the stronger the association between online shopping and time spent bicycling and walking. The research findings can inform planners and decision-makers on the relationships between ICT use and overall travel behavior in order to assess travel demand under different levels of ICT use.

Geographic access to buprenorphine prescribers for patients who use public transit

ObjectiveUrban Medicaid enrollees with opioid use disorder often rely on public transit to reach buprenorphine prescribers. Research has not shown whether public transit provides this population with adequate geographic access to buprenorphine prescribers. We examined travel times to buprenorphine prescribers by car and public transit in urban areas, and determined whether car-based Medicaid regulatory standards produce their intended geographic coverage. MethodsWe obtained data for this study from the Substance Abuse and Mental Health Services Administration's Buprenorphine Practitioner Locator, Microsoft Bing Maps, and the American Community Survey. We examined four urban counties at the centers of the metropolitan statistical areas with the highest 2017 accidental drug poisoning death rates: Kanawha, WV; Montgomery, OH; Philadelphia, PA; and St. Louis City, MO. These counties comprised 696 census tracts representing 1,038,564 households. We calculated travel times from each census tract center to the nearest buprenorphine prescribers by car and public transit, and compared that to 30-min regulatory standards and by whether census tracts had below median levels of car access. We calculated Global Moran's I statistics to determine whether spatial clustering was present among census tracts with limited access to buprenorphine prescribers. ResultsHouseholds in all but two census tracts could access a buprenorphine prescriber within 30 min by car. However, households in 12.1% (84) of census tracts could not do so by public transit. The correlation between car- and public transit-based travel times to the nearest buprenorphine prescriber was 0.11 (95% CI = 0.07–0.22). More than 15% (47,918) of households in the two less densely populated counties could not travel to the nearest prescriber in 30 min and resided in census tracts where access to cars was relatively low. There was no evidence of spatial clustering among census tracts with public transit travel times exceeding 30 min, or among census tracts with public transit travel times exceeding 30 min and below median values of access to cars. ConclusionsGeographic access to buprenorphine prescribers is overestimated by regulatory standards that apply car-based travel time estimates, which are a weak proxy for public transit-based travel times. Since geographic areas with limited access to buprenorphine prescribers do not tend to cluster near one another, individually targeted interventions may be necessary to improve buprenorphine access and utilization.