Increase In Travel Time Research Articles

Assessing Zebra Mussels’ Impact on Fishway Efficiency: McNary Lock and Dam Case Study

The Columbia River Basin faces a threat from the potential invasion of zebra mussels (Dreissena polymorpha), notorious for their ability to attach to various substrates, including concrete, which is common in fishway construction. Extensive mussel colonization within fishways may affect fish passage by altering flow patterns or creating physical barriers, leading to increased travel times, or potentially preventing passage altogether. Many factors affect mussel habitat suitability including vectors of dispersal, water parameters, and various hydrodynamic quantities, such as water depth, velocity, and turbulence. The objective of this study is to assess the potential for zebra mussels to attach to fishway surfaces and form colonies in the McNary Lock and Dam Oregon-shore fishway and evaluate the potential impact of this infestation on the fishway’s efficiency. A computational fluid dynamics (CFD) model of the McNary Oregon-shore fishway was developed using the open-source code OpenFOAM, with the two-phase solver interFoam. Mesh quality is critical to obtain a reliable solution, so the numerical mesh was refined near the free surface and all solid surfaces to properly capture the complex flow patterns and free surface location. The simulation results for the 6-year average flow rate showed good agreement with the measured water column depth over each weir. Regions susceptible to mussel infestation were identified, and an analysis was performed to determine the mussel’s preference to colonize as a function of the depth-averaged velocity, water depth, and wall shear stress. Habitat suitability criteria were applied to the output of the hydraulic variables from the CFD solution and provided insight into the potential impact on the fishway efficiency. Details on the mesh construction, model setup, and numerical results are presented and discussed.

Mapping disparities in head and neck cancer care: Geospatial analysis of social vulnerability and healthcare access in the US.

e23112 Background: Ensuring equitable access to head and neck (H&N) cancer care across the US is vital for optimal patient outcomes. This study explores the impact of social vulnerability on disparities in access to H&N multidisciplinary specialist care by US region and metro status. Methods: Oncologists and a subset of H&N cancer specialist practice locations were extracted from the National Provider Index, American Society of Clinical Oncology, and US News and World Report. Travel time using the fastest transportation mode from population-weighted county centroids to the nearest H&N cancer specialist (surgical, radiation, or medical oncologist) was computed using Travel Time API. Cancer data were obtained from United States Cancer Statistics and Surveillance, Epidemiology, and End Results (SEER). Regression analysis assessed impact of aspects of social vulnerability from the Social Vulnerability Index (SVI) on travel time. A disparity index incorporating travel time, county-level H&N cancer incidence, and SVI was developed to quantify disparities at the county-level. Results: In total, 18,241 medical, 6,387 radiation, and 2,564 surgical oncologists were identified, of which 346 medical, 308 radiation, and 453 surgical oncologists self-identified as H&N specialists. Regression analysis indicated that low household income and lack of insurance within counties significantly increased travel time ( p < .001) after controlling for US region, metro status, cancer rate, and other county-level factors. The disparity index identified counties with highest and lowest disparity (Table 1). Conclusions: Key findings highlight specific counties with higher vulnerability, primarily rural counties in the West and Southeast, and underscores the influence of social vulnerability on access to care. After accounting for US region and metro status, aspects of social vulnerability still predict travel time, with low household income and lack of insurance being the most impactful. Identified disparities in care may provide targets for interventions and policies aimed at reducing access inequalities, such as outreach programs and allocation of resources to underserved areas. [Table: see text]

Is Development Type a Determinant of College and Graduate Students’ Commute Time to School? The Case of Seoul Metropolitan Area

This study examines the impact of large-scale suburban new town development on the commuting time of college and graduate students in the Seoul Metropolitan Area. Household travel diary data from 2016 were analyzed to categorize residential areas and quantify the impacts on commute time to school. Multiple linear regression modeling is used to explore the relationships between development type, individual, and household characteristics and their impact on commute times. The results of the study show that students living in new urban areas have significantly longer commute times than those living in central Seoul, highlighting the differences that result from urban expansion policies targeting middle-class housing. These results suggest that the development of suburban new towns, which was intended to alleviate the housing shortage, has inadvertently lengthened the daily commute time of many students. Thus, a critical reassessment of suburban development strategies is needed to better balance the advantages of residential neighborhoods against the educational and living costs associated with increased travel time.

Real-time scheduling and routing of shared autonomous vehicles considering platooning in intermittent segregated lanes and priority at intersections in urban corridors

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-to-request combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs’ total travel time.

The everyday struggles of accessing public transport for women in the first- and last-mile stretches in Kolkata

Walking and public transport are the primary modes of transport for women in developing countries. However, accessing these is not without barriers, particularly in the first- and last-mile stretches due to poor services, crowding, transfers, waiting, and lack of pedestrian infrastructure. This study aims to understand accessibility and the barriers women face in the first and last miles of everyday commuting. The data for this qualitative study are derived from visual surveys of selected locations in Kolkata and in-depth interviews to understand women's lived experiences, nuances of everyday travel, and diversity of encounters and perceptions. The major barriers to walking and accessing auto-rickshaws and buses are primarily heavy traffic, speeding vehicles, negligent driving, chaotic transfers, crowded conditions of buses, unsafe boarding and alighting, and absence of sidewalks. These barriers make women feel unsafe, influence modal choice, and increase travel time. Challenges in accessibility and acceptability of public transport push women to often make non-efficient and more expensive choices, which affects their work and family life. The findings stress the need for appropriate planning and design strategies and interventions to ensure equitable mobility for women.

The Need of Trustworthy Announcements to Achieve Driving Comfort

An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.

Patient-reported burden associated with pheochromocytoma/paraganglioma diagnosis.

Pheochromocytoma and paragangliomas (PPGLs) originate from the chromaffin cells of the adrenal medulla or neural crest progenitors outside the adrenal gland, respectively. The estimated annual incidence of PPGL is between 2.0 and 8.0/million adults. Minimal data exist on the impact of PPGL from the patient's perspective. Therefore, a survey was adapted from a previously published study on gastroenteropancreatic neuroendocrine tumors to explore the voice of patients with PPGL and learn ways to improve clinical care while understanding the current gaps to direct future research. A self-reported online survey was available to patients with PPGL and those with genetic predisposition even without PPGL from June to July 2022. Survey questions captured sociodemographic and clinical characteristics, the diagnostic workup, treatment and monitoring, quality and access to care, and financial impact. Here, we report the most relevant findings on patient experience of disease burden following diagnosis. A total of 270 people responded, the majority of whom were from the USA (79%), Caucasian (88%), and female (81%). The results of this survey highlight the burden of disease on a patient's daily life, resulting in moderate to severe financial distress, increased travel time to specialized facilities resulting in loss of work and wages, and significant delays in care. Respondents reported being unheard and unacknowledged. With a median time to diagnosis just over 2 years, the physical, mental, and emotional toll are substantial. Increasing access to PPGL specialists and centers could lead to faster diagnoses and better management, which may reduce the burden on both patients and healthcare centers.

Assessment of the Impact of Intercity Bus Station Relocation on Passenger Flows in Urban Public Transport

Problem. The analysis of existing studies on transport hub operation in a city as well as on the methods for modelling the transport demand they generate showed that there are no specific recommendations for assessing the impact of the intercity bus station location on the trip patterns in urban transport system. This caused the need to search for a tool suitable to assess the influence of intercity bus station relocation on passenger service and passenger flows in the urban transport system. This paper shows the results of addressing this issue respective to urban public transport operation. Goal. The goal of the paper is to model the passenger flows to and from intercity bus stations considered transport anchors in urban public transport system as well as estimate the impact of station relocation on passenger flows and quality of service. Methodology. To achieve the stated goal, a survey of intercity passengers at the bus stations in the city of Rivne was made. This allowed for collecting the distances of the trips to and from existing bus stations and fitting the theoretical distribution to describe them. This distribution was used as the deterrence function during trip matrix calculation, which was required for the development of the public transport model. The model made it possible to quantify the potential impact of the Bus Station “Rivne” relocation on the urban transport system. Results. The considered bus station relocation can cause a decrease in the quality of service for passengers travelling to and from the station by public transport due to a probable significant increase in the average travel time and distance, the number of transfers, the passenger hours spent on travelling, and the passenger-kilometres covered to ensure the trips under study. Originality For the first time, the quantitative estimation of the impact of bus station relocation based on the regularities in the lengths of the trips to and from the stations was obtained. Practical value. The obtained trip length distribution can be used to calculate the origin-destination matrix, develop the public transport model, and quantify the probable consequences of the transport hub relocation.

Energy optimization in freight train operations: Algorithmic development and testing

This research applies multi-objective dynamic programming – specifically, goal programming – solved using a computationally efficient heuristic minimum path-finding algorithm (A*) to improve energy efficiency in freight train operations. The investigation focuses on the U.S. freight network, evaluating the impact of the proposed system on six powertrain technologies, namely diesel, biodiesel, diesel-hybrid, biodiesel-hybrid, hydrogen fuel cell, and battery electric on energy consumption and travel time. The primary findings indicate that when prioritizing energy reduction, diesel and biodiesel hybrids emerge as the most effective, achieving a 47% decrease in energy consumption compared to scenarios without optimization. Hydrogen and battery electric technologies demonstrate a 26% energy saving. In contrast, diesel and biodiesel powertrains show the least improvement, with a 21.5% reduction in energy consumption, accompanied by a 60% increase in travel time for all powertrains except hydrogen, which incurs only a 29% increase. Furthermore, when the multi-objective optimization model incorporates travel time, assigning a 70% weight to energy consumption and a 30% weight to travel time, the results are consistent. In this scenario, diesel and biodiesel hybrids yield an 11% reduction in energy consumption, followed by a 7% reduction for hydrogen fuel cells and a 6% reduction for battery electric trains, with diesel and biodiesel powertrains achieving a 5% reduction. This optimization leads to a mere 7% increase in travel time compared to the non-optimized scenario.

Formal validation of authentication scheme in 5G-enabled vehicular networks using AVISPA

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5G-enabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults.

Transit access effectiveness in American metropolitan areas

This research examines transit effectiveness empirically through the lens of access to employment. The approach uses the gradual shift of mobility to access to introduce the Transit-Walk Access Gap (TWAG) as an effectiveness indicator. TWAG is evaluated in the 50 most populated American Metropolitan Areas, and the analyses offer three conclusions. First, transit access effectiveness should not be measured in isolation of spatial and temporal dimensions of the transit service. The results show that transit service is slightly effective in short travel times, but a gradual improvement in transit access effectiveness is noticed by increased travel-time thresholds. Results also indicate that the spatial coverage of effective transit rises with an increase in travel time. However, the nature of the relationship does not appear to be linear. Second, transit access effectiveness is Central Business District (CBD) centric in most American metropolitan areas. The correlation analysis declares that transit access effectiveness declines from a surfeit of effectiveness to relative scarcity as one moves out from the center. It is realized that the correlation becomes more substantial with an increase in the travel-time threshold. Third, transit access effectiveness is positively correlated with transit ridership. The magnitude of association is a function of the metropolitan area and the travel-time threshold, with Memphis as the most elastic and New York the least elastic metropolitan area. The transit access effectiveness indicator introduced here should not be viewed as final. As transit access data are becoming increasingly available and as the goal of transport is shifting from mobility to access, further research needs to be conducted to advance the understanding of access-related transit effectiveness. The adoption of the indicator is, ultimately, a political process.

Multi-objective Optimization Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.

Understanding bike-sharing usage patterns of members and casual users: A case study in New York City

Shared bicycle travel has become an important travel mode for urban residents, and bike-sharing platforms are also booming in major cities worldwide. The bike-sharing platform provides users with systematic services: members refer to annual bike-sharing service subscribers and casual users refer to holders of a day pass or single ride ticket. Even though casual users account for a large share of ridership and revenue at bike-share systems in New York City (NYC), very little is known about the characteristics and preferences of casual users and how they compare to members. Based on the open-docked bike-sharing dataset from Citi Bike, we analyze the bike usage patterns of members and casual users in NYC, and how these patterns change in the face of the COVID-19 pandemic on a typical day level. We find that the COVID-19 pandemic has negatively influenced members' bike trip counts on weekdays; bike travel time increases for members during the pandemic and decreases for casual users after the pandemic. To make a profound study concerning spatial heterogeneities, we employ Gaussian Mixture Model (GMM) to cluster the spatiotemporal changes of the station-level bike usage and obtain four clusters for each user type. Combined with the Points of Interest (POI) information, we find that member-related cluster with commuting POIs is significantly affected by the pandemic, while leisure trips are the most severely affected for casual users. Compared with the central area, peripheral clusters with residential and religious POIs are less affected by the pandemic. According to our findings, new operational strategies such as flexible subscriptions can be developed to attract more users, maintain their stickiness, and improve the bike-sharing level of services.

Spatial Justice of Urban Park Green Space under Multiple Travel Modes and at Multiple Scales: A Case Study of Qingdao City Center, China

Improving the justice of public parks is of great significance to the well-being of residents, and it is also an important goal of green space planning. In this paper, the spatial justice of park green space under five travel modes and at three scales was analyzed using the travel-behavior-based Gaussian two-step floating catchment area method (TB-G2SFCA) and Gini coefficient method for Qingdao City Center. The main results are as follows: Under walking mode, walking–bus mode, and walking–subway mode, there were unserved areas in terms of urban park green space, while there were no unserved areas in the cases of cycling and driving. Residents’ choice of travel time and travel mode would affect the service scope of the park green space, and the increase in travel time would reduce the unserved areas in the urban park green space. The choice of travel time and travel mode affected the accessibility of urban park green space for residents in each residential patch, as well as the justice of the distribution of park green space resources at the scales of street blocks, districts, and the whole study area. The increase in residents’ travel speed and travel time could promote the equitable allocation of urban park green space to a certain extent. The results of this study provide a scientific basis for the planning and construction of urban park green space in Qingdao City.

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

Driver-Centric Urban Route Planning: Smart Search for Parking

With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads to an additional strain on traffic flow, while the search for parking spaces lowers the level of service. In inner cities, vehicles circulate in search of an available parking space, leading to an increase in travel time, fuel consumption, pollutant emissions, and a decrease in traffic safety. The search for a free parking space generates a significant increase in traffic in urban areas. To solve the parking search problem, it is necessary to develop certain strategies and measures that minimize circling in search of a parking space. The implementation of intelligent transportation systems stands out. By applying intelligent transport systems, drivers are provided with information about free parking spaces, which reduces the circulation of vehicles in search of free parking. Although initially ITS systems mainly provided services for closed parking lots and garages, with the further development of the system, the service was extended to street parking lots or open-type parking lots. These measures not only solve traffic challenges but also promote sustainability in urban areas. This article analyzes the effect of a cooperative approach of guiding vehicles to available parking spaces compared to a standard model of searching for an available parking space. Within the framework of the advanced model for searching for available space, four parking demand scenarios were defined and simulated. Based on the created traffic simulation, a comparative analysis was made between the classic and cooperative approach, while the primary differences are manifested in the load of the traffic flow A simulation model was developed using the road network from the urban center of Zagreb.

Which route is better for hazmat transport? An optimised routing index for urban networks

The safety of vehicles and citizens on urban road networks is a major subject, especially if these vehicles carry hazardous materials (hazmats). The paper focuses on developing an optimised index to identify the safest routes between any origin–destination pair within urban networks. The considered parameters include population, traffic safety, volume–capacity ratio, emergency response time, type of hazmat and sensitive and vulnerable places. Notably, this study is the first to incorporate the last two parameters and the combination of all parameters in route optimisation. The research formulates a hazardous material vehicle routing problem and proposes an algorithm to solve it. The approach is applied to a case study in Mashhad city, presenting the safest routes for transporting three types of hazmats. The findings indicate that the reduction in average risk score is comparable with the increase in average travel time; however, gain of the safest routes is lower than loss of the longer distance. The proposed method is practical and applicable to diverse urban networks, aiding in the routing and planning of any hazmat-type transportation.

Study on the network acoustics environment effects of traffic management measures by a bilevel programming model

This study examines the mechanisms of environmentally oriented traffic management measures through an environmental network design problem. An improved user equilibrium (UE) model was proposed to simulate the traffic state under traffic management measures and combine a traffic noise prediction model to obtain the noise distribution, while the environmental noise functional zone compliance was considered as the objective. A bilevel programming model was established to find the optimal solution for the acoustic environment and traffic under different strategy spaces. As results, with sufficient strategy space, speed limits can increase the compliance rate of the environmental noise functional zones from 53.7% to 100%, and flow limits can increase the compliance rate by 7.1%. With insufficient strategy space, collaborative control can achieve better environmental optimization than speed and flow limits, but there are serious traffic efficiency costs which travel time increases of 24%–45%. This is due to the detour phenomena under flow limits. In addition, road classification is an important control reference and coupling effects occur during the control of speed and flow limits. This study of the mechanism underlying the optimization of the acoustic environment under traffic management measures can provide theoretical guidance for planners.

Determining optimal location and size of PEV fast-charging stations in coupled transportation and power distribution networks considering power loss and traffic congestion

The placement and sizing of plug-in electric vehicle fast-charging stations (PEVF-CS) can significantly influence traffic flow in urban transportation networks. Consequently, suboptimal choices regarding the location and size of PEVF-CSs may lead to increasing travel time and traffic congestion in transportation networks and deterioration of the power quality indexes in power distribution networks. Also, each PEVF-CS is equipped with power electronic converters that can be used as power line conditioners and active filters to compensate for the reactive power and current harmonic components due to nonlinear loads and reduce the power loss. Thus, PEVF-CSs can improve the voltage profile, decrease voltage THD, and reduce the power loss in power distribution networks. Accordingly, this paper develops a mixed-integer linear programming model (MILP) to determine the optimal locations and sizes of PEVF-CSs. In addition, the proposed model considers traffic congestion and power quality in the coupled transportation and power distribution networks. The model considers various load profiles and origin-destination demands to address the different operational aspects of transportation and power distribution networks. The main features of the proposed model are 1- road congestion reduction, 2- traveling time reduction, 3- power loss reduction, and 4- power quality improvement. The proposed model is implemented using GAMS software and applied to different-scale test systems in different scenarios. The results show that the proposed method decreases traffic congestion and improves power quality.

Demographics, distance to gastrointestinal specialists, and social deprivation are associated with advanced stage of gastrointestinal cancer diagnosis.

Gastrointestinal (GI) luminal cancers can be detected at early stages by endoscopic procedures. Place-based factors, such as social deprivation and distance to specialist care, are under-investigated with regard to the stage of diagnosis. This was a retrospective cohort study among persons ≥18 years of age in the Florida Cancer Data System, a population-based cancer incidence registry. We included persons diagnosed with esophageal cancer, gastric canceror colorectal cancer, with at least 1 measure of geographic location during the period January 1, 1981, to December 31, 2016. Multivariate multinomial logistic regression was used to identify factors associated with the stage of diagnosis, including social deprivation and proximity to GI care. Among 379,054 persons, the median age was 71 years, and 54% were male. Distant stage disease was significantly less likely than local stage in those of non-Hispanic/Latino ethnicity (odds ratio [OR] 0.92, 95% confidence interval [CI] 0.89-0.94, P<0.001). Distant disease was more likely in African Americans (OR 1.30, 95%CI 1.26-1.34) and Asians (OR 1.41, 95%CI 1.27-1.56, P<0.001), with each 5-min increase in travel time to specialists, (OR 1.02, 95%CI 1.01-1.02, P<0.001), and with each 10-point increase in Social Deprivation Index (OR 1.01, 95%CI 1.01-1.02, P<0.001). A greater distance from care and living in areas with increased deprivation are associated with an advanced stage of diagnosis and should be recipients of policy-driven efforts to improve access to care. That the strongest risk factors include minority race and ethnicity underlines the complexity of healthcare disparities.