Bureau Of Transportation Statistics Research Articles

Machine learning insights into regional dynamics and prevalence of COVID-19 variants in US health and human services regions

BackgroundThe COVID-19 pandemic arising from the emergence of SARS-CoV-2 in late 2019 has led to global devastation with millions of lives lost by January 2024. Despite the WHO’s declaration of the end of the global health emergency in May 2023, the virus persists, propelled by mutations. Variants continue to challenge vaccination efforts, underscoring the necessity for ongoing vigilance. This study aimed at contributing to a more data-driven approach to pandemic management by employing random forest regression to analyze regional variant prevalence.MethodsThis study utilized data from various sources including National COVID Cohort Collaborative database, Bureau of Transportation Statistics, World Weather Online, EPA, and US Census. Key variables include pollution, weather, travel patterns, and demographics. Preprocessing steps involved merging and normalization of datasets. Training data spanned from January 2021 to February 2023. The Random Forest (RF) Regressor was chosen for its accuracy in modeling. To prevent data leakage, time series splits were employed. Model performance was evaluated using metrics such as MSE and R-squared.ResultsThe Alpha variant was predominant in the Southeast, with less than 80% share even at its peak. Delta surged initially in Kansas City and maintained dominance there for over 5 months. Omicron subvariant BA.5 spread nationwide, becoming predominant across all Health and Human Services regions simultaneously, with New York seeing the earliest and fastest decline in its share. Variant XBB.1.5 concentrated more in the Northeast, but limited data hindered full analysis. Using RF Regressor, key features affecting spread patterns were identified, with high predictive accuracy. Each variant showed specific environmental correlations; for instance, Alpha with air quality index, Delta with ozone density, BA.5 with UV index, and XBB.1.5 with land area and income. Correlation analysis further highlighted variant-specific associations.ConclusionsThis research provides a comprehensive analysis of the regional distribution of COVID-19 variants, offering critical insights for devising targeted public health strategies. By utilizing machine learning, the study uncovers the complex factors contributing to variant spread and reveals how specific factors contribute to variant prevalence, offering insights crucial for pandemic management.

Health and equity impacts from transportation noise

Transportation noise is a widespread issue throughout the developed world and is some places is considered a more significant issue than crime. The World Health Organization (WHO) has evaluated the effects of transportation noise on public health in Europe, however, there is no equivalent research in the United States. This paper evaluates transportation noise exposure in the United States, compares this exposure to WHO recommendations, and identifies population groups that are most likely to be affected through a case study. This study uses publicly available resources from the Bureau of Transportation Statistics and the Environmental Protection agency.

The Effect of the COVID-19 Pandemic on Financial Performance of Full-Service Versus Low-Cost Airlines

This study analyzes the post-pandemic recovery of the low-cost and full-service airline industries. An analysis of the financial performance of five full-service and four low-cost airlines during and after the pandemic is conducted using four different financial ratios. Low-cost airlines recovered faster and had better performance after the pandemic, despite receiving less federal aid. To explain this difference in recovery speed, the author examined the air traffic, market share, fleet, route structure/choice of airports, and employee productivity between low-cost and full-service airlines using data from the Bureau of Transportation Statistics (BTS) from 1998 to 2022.

Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation

Human factors play a huge role in road traffic safety. Research has found that a huge proportion of traffic crashes occur due to some form of human error. Improving road user behavior has been the major strategy that has been emphasized for improving road traffic safety. Meanwhile, despite the training efforts, and testing for drivers, the global status of road traffic safety is alarming. This research highlights the seriousness of human factors on road traffic safety and provides actionable strategies to greatly reduce the negative impact of human factors on road traffic safety. Motor vehicle safety data that were made available online by the U.S. Bureau of Transportation Statistics were reviewed to evaluate the severity of traffic collisions. To evaluate the extent of human factors in motor vehicle traffic fatalities, data for Canadian motor vehicle traffic collision statistics were reviewed. The study confirms that human factors (such as driver distraction, fatigue, driving under the influence of drugs and alcohol etc.) play a huge role in road traffic fatalities. The need for a reasonable degree of automation to help reduce the impacts of human factors on road safety and recommendations aimed at providing widespread support for a reasonable degree of automation systems in driving tasks are presented. Actionable strategies that can be implemented by policymakers to reduce global road traffic fatalities are also presented.

Forecasting air passenger traffic and market share using deep neural networks with multiple inputs and outputs.

In this study, we address the challenge of accurate time series forecasting of air passenger demand using historical market demand data from the U.S. commercial aviation industry in the 21st century. Commercial aviation is a major contributor to the U.S. economy, directly or indirectly generating ~US$1.37 trillion annually, or 5% of annual GDP, and supporting more than 10 million jobs (Airlines for America, 2024). Over 1 billion passengers flew through U.S. airports in 2023 (Bureau of Transportation Statistics, 2024a). Using multiple correlated time series inputs predicts future values of multiple interrelated time series and leverages their mutual dependencies to enhance accuracy. In this study, we introduce a two-stage algorithm employing a deep neural network for correlated time series forecasting, addressing scenarios where multiple input variables are interrelated. This approach is designed to capture the influence that one time series can exert on another, thereby enhancing prediction accuracy by leveraging these interdependencies. In the first stage, we fit four Recurrent Neural Network (RNN) models to generate accurate univariate forecasts, each functioning as a single input-output model to predict aggregated market demand. The Gated Recurrent Unit (GRU) model was the top performer for our dataset overall. In the second stage, we apply the best fitted model (GRU Model) from Stage 1 to each individual competitor (disaggregated from the market) and then merge all input tensors using the Concatenate function. We hope to contribute to the relevant body of knowledge with a deep neural network framework for forecasting market share among competitors in the U.S. commercial aviation industry, as no similar approach has been documented in the literature. Given the importance of the industry, there is potentially great value in applying sophisticated forecasting techniques to achieve accurate predictions of air passenger demand. Moreover, these techniques may have wider applications and can potentially be employed in other contexts.

A matheuristic for aircraft maintenance routing problem incorporating cruise speed control

Aircraft maintenance routing problem incorporating cruise speed control (AMRP-CSC) is an extension of the well-known aircraft maintenance routing problem (AMRP) where the flexible cruise times of flights are considered in route construction. Changing cruise times in AMRP can transform the infeasible flight connections into feasible ones, resulting in a larger solution space and further opportunities for efficiently routing. This may open up the possibility for a substantial improvement in aircraft utilization, but also increases resolution complexity. In this study, a new solution methodology, namely a matheuristic approach, is proposed for this problem. It is composed of three main components: an improved ant colony optimization (IACO) algorithm, a set partitioning (SP) procedure, and a neighborhoods search (NS) procedure. The IACO algorithm serves as a route generator, populating a pool of routes with promising feasible aircraft maintenance routes. Then, a SP model, which features the high-quality columns corresponding to the routes in the pool, is solved to produce a possible better solution. Finally, this solution is further improved by a NS procedure that iteratively solves the reduced AMRP-CSC instances to optimality. This matheuristic approach is analyzed and tested using the data extracting from the Bureau of Transportation Statistics (BTS), and then its accuracy and the efficiency have been demonstrated by experiment analyses.

Delivery of Obecabtagene Autoleucel (obe-cel, AUTO1) for the FELIX Pivotal Study Demonstrating Robust Cell Processing, Robust Release Testing, and Reliable Logistics, Together with Readiness for Sustainable Patient (pt) Care

Background: Obe-cel is an autologous chimeric antigen receptor (CAR) T cell product whose CD19 binding domain has a unique fast off-rate and was designed for improved safety and persistence over existing CD19 CAR T therapies. Successful CAR T therapy relies on a rapid and effective end-to-end process. The challenge for product manufacturing is twofold: first, pts with high tumor burden can have T cells that are highly differentiated and exhausted; second, pts with leukemic cells in circulation have apheresis containing a substantial proportion of leukemic cells that require removal before manufacture can start. Having robust, reliable manufacturing, testing, logistics processes, and teams that are able to provide a consistent CAR T product is key to achieving timely vein-to-certification/vein-to-delivery (V2C/V2D) targets. This abstract reports on the manufacturing, quality control (QC) and logistics processes set up for the FELIX study, and the impact of on-study optimization for scale up of production. Methods: The FELIX clinical study design required manufacturing, testing and logistics processes that could support a global, multicenter study with a target infusion of ~90 pts. The key elements were: 1) manufacturing and testing location: obe-cel was manufactured in Stevenage, UK, chosen because of proximity to a major international airport (London Heathrow) that provides logistics access to Europe and the US, and the ability to leverage existing knowledge and talent; 2) manufacturing process: manufacturing was carried out 7 days/week, was highly automated with mainly closed-system processing, using a Miltenyi Biotec Prodigy ® (https://www.miltenyibiotec.com), 3) analytical procedures: QC procedures, including measuring biological activity and sterility testing, were carried out in-house to reduce turnaround time (TAT); 4) logistics: TAT was minimized and reliability improved by establishing primary and secondary routes plus turnkey contingency plans with courier and charter flight options 5) targets: V2C (time from leukapheresis to quality release) and V2D (time from leukapheresis to delivery of product to the hospital) of ~23 and ~25 days, respectively, for the FELIX study. Results: Between Jul 2021 and Dec 2022 (FELIX Phase II Cohort A), the 32 clinical sites for FELIX in the US, UK and Spain were all supplied obe-cel from the UK. Median V2C and V2D times were 21 and 24 days, respectively. Despite the median leukemic B-cell content in apheresis being 21% (range 0-97%) and the median CD3+ T cell content being 13% (range 1-91%), 96% of manufactured obe-cel batches reached their target dose of 410 x 10 6 CAR T cells. From apheresis to product the median (range) percentage of naïve T cells increased from 20% (1-73%) to 31% (2-74%) while the medians for T CM and T EM remained unchanged at 13% to 13% and 19% to 17%, respectively, and the median TEMRA content decreased from 37% (range 4-93%) to 31% (range 5-90%), indicating that the manufacturing process managed to drive towards a less differentiated T cell phenotype in the final drug product. Transduction rates were consistently high with a median of 70% (range 14-87%). In the morphological Phase II Cohort A of the FELIX study, 112 pts were leukapheresed and 94 (84%) were dosed. All leukapheresis collections and cell product deliveries were executed successfully and with the expected quality, despite the challenges posed by the COVID-19 pandemic. In addition, US international airline flights decreased by 43 % (US Dept of Transportation, Bureau of Transportation Statistics; July 2023) compared with pre COVID-19 pandemic, but sample collection and product delivery were successfully maintained, with no batches impacted. Maintaining consistently short QC and release times were driven by streamlined analytical strategy and batch records. Conclusions: The FELIX study successfully demonstrated the robust operability of obe-cel manufacturing, QC and logistics processes, meeting target V2C and V2D. All apheresis starting material successfully processed despite the multitude of constraints posed by the COVID-19 pandemic. Further optimization and improvements made during the study increased reliability, consistency and precision of the manufacturing process, and supported the development of a new obe-cel manufacturing facility with greater production capacity that aims to achieve a ≥95% manufacturing success rate with ≤15-day V2C times.

An optimization approach to financial and operational viabilities of spare aircraft

PurposeThe airlines cancel their flights frequently because of factors that they do not have any control over. Spare aircraft can potentially address some of the issues caused by cancelled flights. This paper aims to offer an exploratory study into the financial and operational viabilities of spare aircraft for airlines.Design/methodology/approachMathematical models are proposed to evaluate the financial and operational metrics under different scenarios. The models are applied to Delta, Spirit and Southwest Airlines with different business models. All data are extracted from US Bureau of Transport Statistics, Cirium Diio Mi and CAPA databases. The IBM Cplex solver was used to execute the binary linear program models.FindingsThe research revealed that factors such as airline network size, hub and spoke structure and average weekly flight cancellations are crucial in establishing the need for spare aircraft. For the number of weekly cancellations, there exist break-even values that reasonably justify spare aircraft.Practical implicationsModels can be customized and applied to other modes of transportations.Originality/valueThis study is the first to consider the use of spare aircraft in airlines from both financial and operational perspectives within the scope of the mathematical model. The analyses identify financial break-even points for a number of spare aircraft and their home base locations for three airlines. Operational utilization of spare aircraft is studied and contrasted with financial metrics.

Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.

Impact of Remote Cardiac Monitoring on Greenhouse Gas Emissions: Global Cardiovascular Carbon Footprint Project

BackgroundRemote monitoring (RM) of patients with cardiac implantable electronic devices (CIEDs) is efficient and requires fewer resources than conventional monitoring. However, the impact of RM on the carbon footprint (CF) is not known. ObjectivesThe authors sought to evaluate the reduction in cost and greenhouse gas (GHG) emissions with RM as compared to conventional monitoring of CIEDs and its relevance to CF. MethodsData were obtained from a third-party RM provider on 32,811 patients from 67 device clinics across the United States. Distance from home address to the device clinic for patients on RM was calculated. Savings in total distance traveled over 2 years was calculated using frequency of follow-up required for the device type. National fuel efficiency data and carbon emission data were obtained from the Bureau of Transportation Statistics and U.S. Environmental Protective Agency, respectively. The average gas price during the study period was obtained from U.S. Energy Information Administration. ResultsIn the study population, RM resulted in a total saving of 31.7 million travel miles at $3.45 million and reduction of 12,518 metric ton of GHG from gasoline. There was a reduction of 14.2-million-page printouts, $3 million in cost, and 78 tons of GHG. Improvement in workforce efficiency with RM resulted in savings of $3.7 million. There was a net saving of $10.15 million and 12,596 tons of GHG emissions. ConclusionsRM of patients with a CIED resulted in significant reductions in GHG emissions. Efforts to actively promoting RM can result in significant reduction in CF.

Multivariate regression analysis of airline market recovery in the post-pandemic era

While COVID-19 has substantially affected the airline industry, a business rebound is expected. To better anticipate the needed workforce, this study revisited economic datasets from the Federal Reserve Bank, Bureau of Transportation Statistics (BTS), the Federal Aviation Administration (FAA), and other essential resources to predict the market recovery. VOSviewer and multivariate regression analysis were used to identify critical research clusters and important variables of the emerging market recovery. Minitab was used to run the multivariate regression analysis and the correlation coefficients among selected variables were discovered. The result showed that strongly correlated variables for airline market recovery included Net Domestic Product (NDP), Gross Domestic Product (GDP), and Real Disposable Personal Income (RDPI) in this study.

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays. The proposed model is estimated using 2019 marketing carrier on-time performance data compiled by the Bureau of Transportation Statistics (BTS) for 67 airports in the continental U.S. The delay data is augmented with a comprehensive set of independent variables including traffic conditions at the origin and destination airports in the hours preceding flight departure and arrival, trip-level attributes, weather variables for the entire flight duration, and spatial and temporal factors. The model estimation results highlight that the Joe copula model with parameterization provides the best data fit. The model performance is further established to be excellent using a holdout sample. Finally, to illustrate the applicability of the model for prediction and highlight the impact of independent variables, we perform a prediction exercise under a host of hypothetical scenarios. The illustration provides a mechanism for employing the proposed model as a tool for airline-carrier-level or airport-level delay prediction analysis using weather forecasts while controlling for a host of independent variables.

Renewable Energy System: Alternative Fuel Usage in the Energy Market of the US Public Transportation System

The U.S. transportation sector is the second largest energy consumer in 2020 with about a 35% usage rate. However, the sector accounted for the largest proportion (29%) of the total greenhouse gas emissions in 2019. Compared with the use of personal automobiles, public transportation is believed to be cost-effective and provides low emissions, which tends to reduce the need to travel long distances and lower the carbon footprints of transit operations. This study examined the impact of alternative fuels (various blends of biodiesel, hydrogen, methanol, and ethanol) versus conventional fuels on greenhouse gas emissions in the U.S. public transit system. The research was anchored by three regression models to analyze the joint and individual impact of both conventional fuel and alternative fuel. Secondary data were extracted from the American Public Transit Association 2021 Fact Book, the U.S. Bureau of Transportation Statistics, and the Inventory of U.S. Greenhouse Gas Emissions and Sinks 1999-2019 (EPA 2021). The descriptive statistics, correlation, and stationarity tests were established on the variables, and the regression models were estimated using ordinary least squares. The findings revealed that while conventional fuel expands the flow of carbon emissions, alternative fuel is a drag on the total emissions from public transportation. However, it was concluded that the utilization of alternative fuels in the U.S. transportation industry is still very low, thus there is a need to put all machinery in motion to embrace this fuel type to reverse climate change issues, especially in the public transit system.

Adherence to Selected Air Carrier (Airline) Operational Regulations for Improved General Aviation Flight Safety in Degraded Visibility

Introduction: General aviation largely comprises fixed-wing piston-engine light aircraft (,12,500 lbs). Unfortunately, this civil aviation sector suffers a vastly inferior safety record when compared with air carriers (60- to 80-fold higher accident rate). Additionally, such mishaps pose a considerable financial burden to both the affected family and the United States: US$1.64–4.64 billion annually. We hypothesize that this safety disparity partly reflects more stringent operational regulations for air carriers. Herein, we determined whether compliance with six selected air carrier regulations could potentially reduce general aviation accidents in degraded visibility (IMC) the majority of which are fatal. Methods: Accidents (2005–2019) were identified from the National Transportation Safety Board Access database. Fleet data for rate calculations were per the general aviation survey and the Bureau of Transportation Statistics. Statistics used Poisson distributions. Results: Of 219 general aviation accidents in IMC, 43 (20%) could potentially have been averted had one, or more, of the selected air carrier regulations been complied with. The largest percentage (62%) of the 43 mishaps were due to pilots operating contrary to the air carrier regulation specifying takeoff or landing weather minimums. The second largest group related to more conservative weather minimums required for an inexperienced airline pilot-in-command, eschewed in 19% of preventable general aviation IMC mishaps. Conclusions: Alignment with the aforementioned air carrier operational rules could potentially blunt the IMC accident rate (by 20%) for general aviation. Practical Applications: Adherence to the aforementioned air carrier regulations should be advocated to general aviation pilots operating in IMC.

The impact of the COVID-19 crisis on the US airline market: Are current business models equipped for upcoming changes in the air transport sector?

We address the impact of the COVID-19 crisis on the US airlines market and discuss the benefits and limitations of current business models in a context of increasing socio-economic uncertainty and stringent environmental regulations. Drawing our data from the Bureau of Transportation Statistics and 10-K/A reports, we undertook an exploratory study of the performance of the 10 main passenger airlines in their domestic operation during one year. We found that, although major losses occurred industry-wide, ultra-low-cost and low-cost airlines fared better than full-service network carriers did in terms of financial performance. We argue, nevertheless, that such apparently successful business models are not necessarily adaptive to address future industry changes.

Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model.

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

Code-sharing agreements and path quality in the US airline industry

When appraising code-sharing partnerships, policymakers are primarily interested in how such marketing arrangements affect prices. Expectedly, the pricing effects of code-sharing have been abundantly studied. This paper examines the impact of codeshare agreements on the itinerary routing of airline products in U.S. domestic markets. Using data from the U.S. Bureau of Transportation Statistics, we identify two main findings. First, consistent with the literature, we find that the overwhelming majority of domestic code-share itineraries involve a single operating carrier, a practice we refer to as virtual code-sharing. Second, and most importantly, we find that virtual (traditional) codeshare itineraries are associated with better (worse) path quality relative to itineraries marketed and operated by a single carrier in the same market. The path quality effects we find come from different sources. In the case of virtual code-sharing, the positive path quality effects come from both short- and long-haul markets whereas in the case of traditional code-sharing, the negative path quality effects originate from long-haul markets.

Formation of aspects of social and physical safety of employees of the railway complex through the introduction of automated processes

This article is devoted to the analysis of the effectiveness of the integration of automated processes in the functioning of the railway sector on the social and physical safety of employees of railway segment. Improving the safety of railway workers is one of the key goals of railway companies all around the world, which is associated with the increased danger of work related to railway tracks. During writing this scientific article, the authors used the following theoretical methods: integration of research data conducted in France, the Netherlands, Slovakia, the Czech Republic, Germany, Norway, and Russia; review of scientific sources using key databases, including Scopus, Web of Science, Bureau of Transportation Statistics; analysis of practices for the implementation of automated systems and existing concepts used in different countries. The authors also classified of existing types of railway workers’ safety and identified the relation between the introduction of automated process systems and the level of safety of railway workers. The results of this research allow us to identify the correlation between the introduction of automated processes in the functioning of railway companies and the social and physical safety of employees and passengers of the railway sector.

A closer look at rail methodology in the BTS National Transportation Noise Map

In 2017, the Bureau of Transportation Statistics released the inaugural national, multi-modal transportation noise map prototype. The noise modeling and mapping effort was envisioned as a way to facilitate the geographic tracking of national trends and provide insight into transportation noise-related questions as changes occur over time - changes between modes, types of vehicles within modes and the geographic shifts of populations. How do changes in aircraft technology change the transportation noise landscape? Does increased high speed rail availability affect highway-related noise? How does a population shift away from urban centers affect the soundscape? The inaugural model included aviation and highway sources. The first update, released in November 2020, includes passenger rail-related noise in addition to aviation and highway sources. Operations in this new mode include commuter rail mainline, high-speed electric, light rail, heavy rail and streetcars, along with commuter rail horns at highway-rail grade crossings. The data for this noise map were modeled based on USDOT methods, with adjustments and simplifications to model on a national scale. This paper focuses on the modeling methods and geospatial approach used to develop the passenger rail noise data layer.

Travel-Related Economic Burden of Chimeric Antigen Receptor TCell Therapy Administration by Site of Care.

IntroductionWe previously examined how expanding access to chimeric antigen receptor (CAR) T cell therapy administration sites impacted patient travel distances and time. In the current study, we estimated travel-related economic burden associated with site-of-care options among patients with relapsed/refractory diffuse large B cell lymphoma.MethodsWe used geographic information system methods to quantify travel-related economic burden across three site-of-care scenarios: academic hospitals; academic and community multispecialty hospitals; and academic and community multispecialty hospitals plus nonacademic specialty oncology network centers. Socioeconomic status, administration sites, and county of residence were derived from the US Census Bureau and publicly available sources. Travel costs were based on governmental guidelines, US census wage data, and Bureau of Transportation Statistics. Travel distance and time to the nearest CAR T cell therapy administration sites were estimated from previous research.ResultsTotal national estimated costs associated with traveling for CAR T cell therapy were $21.1 million if CAR T cell therapy was offered exclusively in academic hospitals, and $14.7 million if expanded to include community hospitals plus nonacademic specialty oncology network centers, representing a $6.5-million reduction associated with expanding access to eligible patients. The largest cost-saving component was lodging/meals. Regional and demographic cost differences were statistically significant between academic hospitals and nonacademic hospitals/specialty oncology centers. In all scenarios, patients living below the federal poverty level (FPL) had higher weighted mean total costs versus patients living above the FPL. White and Native American patients were estimated to have the highest weighted mean total costs across race/ethnicity groups. For all subgroups, costs were reduced by expanding access beyond academic hospitals.ConclusionCAR T cell therapy is currently restricted to academic hospitals; total travel costs could be substantially decreased if access is expanded to nonacademic hospitals and specialty oncology centers. Patients in rural areas and those living below the FPL are particularly disadvantaged by restricted access.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-021-01839-y.