This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced ...conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.
Percent change in roadway speed in San Francisco between 2010 and 2016 attributable to each of five factors.
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Traffic congestion has worsened noticeably in San Francisco and other ...major cities over the past few years. This change could reasonably be explained by strong economic growth or other standard factors such as road and transit network changes. However, the worsening congestion also corresponds to the emergence of Transportation Network Companies (TNCs), such as Uber and Lyft, raising the question of whether the two trends may be related. Our research decomposes the contributors to increased congestion in San Francisco between 2010 and 2016, considering contributions from five incremental effects: road and transit network changes, population growth, employment growth, TNC volumes, and the effect of TNC pick-ups and drop-offs. We do so through a series of controlled travel demand model runs, supplemented with observed TNC data collected from the Application Programming Interfaces (APIs) of Uber and Lyft. Our results show that road and transit network changes over this period have only a small effect on congestion, population and employment growth each contribute about a quarter of the congestion increase, and that TNCs are the biggest contributor to growing congestion over this period, contributing about half of the increase in vehicle hours of delay, and adding to worsening travel time reliability. This research contradicts several studies that suggest TNCs may reduce congestion, and adds evidence in support of other recent empirical analyses showing that their net effect is to increase congestion. It is more data rich and spatially detailed than past studies, providing a better understanding of where and when TNCs add to congestion. This research gives transportation planners a better understanding of the causes of growing congestion, allowing them to more effectively craft strategies to mitigate or adapt to it.
People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and ...glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open.
We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies.
We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants-which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind.
Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant-health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data.
The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups.
This study was undertaken to determine if crosstalk among the transient receptor potential (TRP) melastatin 8 (TRPM8), TRP vanilloid 1 (TRPV1), and vascular endothelial growth factor (VEGF) receptor ...triad modulates VEGF-induced Ca
signaling in human corneal keratocytes. Using RT-PCR, qPCR and immunohistochemistry, we determined TRPV1 and TRPM8 gene and protein coexpression in a human corneal keratocyte cell line (HCK) and human corneal cross sections. Fluorescence Ca
imaging using both a photomultiplier and a single cell digital imaging system as well as planar patch-clamping measured relative intracellular Ca
levels and underlying whole-cell currents. The TRPV1 agonist capsaicin increased both intracellular Ca
levels and whole-cell currents, while the antagonist capsazepine (CPZ) inhibited them. VEGF-induced Ca
transients and rises in whole-cell currents were suppressed by CPZ, whereas a selective TRPM8 antagonist, AMTB, increased VEGF signaling. In contrast, an endogenous thyroid hormone-derived metabolite 3-Iodothyronamine (3-T
AM) suppressed increases in the VEGF-induced current. The TRPM8 agonist menthol increased the currents, while AMTB suppressed this response. The VEGF-induced increases in Ca
influx and their underlying ionic currents stem from crosstalk between VEGFR and TRPV1, which can be impeded by 3-T
AM-induced TRPM8 activation. Such suppression in turn blocks VEGF-induced TRPV1 activation. Therefore, crosstalk between TRPM8 and TRPV1 inhibits VEGFR-induced activation of TRPV1.
As a treatment option for people living with diabetes, automated insulin delivery (AID) systems are becoming increasingly popular. The #WeAreNotWaiting community plays a crucial role in the provision ...and distribution of open-source AID technology. However, while a large percentage of children were early adopters of open-source AID, there are regional differences in adoption, which has prompted an investigation into the barriers perceived by caregivers of children with diabetes to creating open-source systems.
This is a retrospective, cross-sectional and multinational study conducted with caregivers of children and adolescents with diabetes, distributed across the online #WeAreNotWaiting online peer-support groups. Participants-specifically caregivers of children not using AID-responded to a web-based questionnaire concerning their perceived barriers to building and maintaining an open-source AID system.
56 caregivers of children with diabetes, who were not using open-source AID at the time of data collection responded to the questionnaire. Respondents indicated that their major perceived barriers to building an open-source AID system were their limited technical skills (50%), a lack of support by medical professionals (39%), and therefore the concern with not being able to maintain an AID system (43%). However, barriers relating to confidence in open-source technologies/unapproved products and fear of digital technology taking control of diabetes were not perceived as significant enough to prevent non-users from initiating the use of an open-source AID system.
The results of this study elucidate some of the perceived barriers to uptake of open-source AID experienced by caregivers of children with diabetes. Reducing these barriers may improve the uptake of open-source AID technology for children and adolescents with diabetes. With the continuous development and wider dissemination of educational resources and guidance-for both aspiring users and their healthcare professionals-the adoption of open-source AID systems could be improved.
Aims
Several commercial and open‐source automated insulin dosing (AID) systems have recently been developed and are now used by an increasing number of people with diabetes (PwD). This systematic ...review explored the current status of real‐world evidence on the latest available AID systems in helping to understand their safety and effectiveness.
Methods
A systematic review of real‐world studies on the effect of commercial and open‐source AID system use on clinical outcomes was conducted employing a devised protocol (PROSPERO ID 257354).
Results
Of 441 initially identified studies, 21 published 2018–2021 were included: 12 for Medtronic 670G; one for Tandem Control‐IQ; one for Diabeloop DBLG1; two for AndroidAPS; one for OpenAPS; one for Loop; three comparing various types of AID systems. These studies found that several types of AID systems improve Time‐in‐Range and haemoglobin A1c (HbA1c) with minimal concerns around severe hypoglycaemia. These improvements were observed in open‐source and commercially developed AID systems alike.
Conclusions
Commercially developed and open‐source AID systems represent effective and safe treatment options for PwD of several age groups and genders. Alongside evidence from randomized clinical trials, real‐world studies on AID systems and their effects on glycaemic outcomes are a helpful method for evaluating their safety and effectiveness.
Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of ...detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.
Transportation network companies (TNCs) provide vehicle-for-hire services. They are distinguished from taxis primarily by the presumption that vehicles are privately owned by drivers. Unlike taxis, ...which must hold one of approximately 1,800 medallions licensed by the San Francisco Municipal Transportation Agency (SFMTA) to operate in San Francisco, there is no regulatory limit on the supply of TNCs. TNCs have an increasingly visible presence in San Francisco. However, there has been little or no objective data available on TNCs to allow planners to understand the number of trips they provide, the amount of vehicle miles traveled they generate, or their effects on congestion, transit ridership, transit operations, or safety. Without this type of data it is difficult to make informed planning and policy decisions. Discussions with Uber, Lyft, and the California Public Utilities Commission, which collects trip-level data from TNCs in California, requesting information on TNC trips have not resulted in any data being shared. Under increasing pressure from policymakers for objective data to inform policy decisions, the San Francisco County Transportation Authority (SFCTA) partnered with researchers from Northeastern University who developed a methodology for collecting data through Uber’s and Lyft’s application programming interfaces (APIs) with high spatial and temporal resolution. This paper provides a brief literature review on transport network company (TNC) data, and goes one to describe the methodology used to collect data, summarizes the process for converting the raw data into estimated TNC trips, and presents an analysis of the results of the TNC trip estimates. This study determined that TNCs serve a substantial number of trips in San Francisco, over 170,000 on a typical weekday, that these trips follow traditional time of day distributions, and that they tend to take place in the busiest parts of the City.
Where ridehail drivers go between trips Millard-Ball, Adam; Liu, Liwei; Hansen, Whitney ...
Transportation (Dordrecht),
10/2023, Letnik:
50, Številka:
5
Journal Article
Recenzirano
Odprti dostop
We analyze what ridehail drivers do when searching for paid fares. We use a dataset of 5.3 million trips in San Francisco and partition each search trip into cruising, repositioning, and parking ...segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find evidence of racial disparities, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color.
Transportation network companies (TNCs) such as Uber and Lyft have grown tremendously over the last decade, particularly in the San Francisco Bay Area. Nonetheless, relatively little publicly ...available data exist about the users of these services, their travel behaviors, volume of use, the times and locations of TNC trips, and how TNC services are affecting transportation system performance overall. This paper describes the methods and descriptive results of the first large-scale smartphone-based TNC user survey conducted in the California Bay Area in the fall 2018 and spring of 2019.
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