scholarly journals Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

2021 ◽  
Author(s):  
Naveen Raman ◽  
Sanket Shah ◽  
John Dickerson
Keyword(s):  
New York ◽  
New York City ◽  
State Of The Art ◽  
Data Driven ◽  
Free Rider Problem ◽  
Fairness And Efficiency ◽  
Disparate Treatment ◽  
Fairness Constraints ◽  
Unequal Income Distribution ◽  
Income Variability

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce unfairness either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: by incorporating fairness constraints into the objective function and redistributing income to drivers who deserve more. To test these out, we use New York City taxi data to evaluate their performance on both the rider and driver side. For the first method, we find that optimizing for driver fairness out-performs state-of-the-art models in terms of the number of riders serviced, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a method to combat income inequality by having drivers keep an $r$ fraction of their income, and contribute the rest to a redistribution pool. For certain values of $r$, most drivers earn near their Shapley value, while still incentivizing drivers to maximize income, thereby avoiding the free-rider problem and reducing income variability. While the first method is useful because it improves both rider and driver-side fairness, the second method is useful because it improves fairness without affecting profitability, and both methods can be combined to improve rider and driver-side fairness.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

scholarly journals Data, democracy and school accountability: Controversy over school evaluation in the case of DeVasco High School

2017 ◽  
Vol 4 (1) ◽  
pp. 205395171770240 ◽  
Author(s):  
John West
Keyword(s):  
High School ◽  
New York ◽  
New York City ◽  
Small Schools ◽  
Central City ◽  
Department Of Education ◽  
Data Driven ◽  
School Evaluation ◽  
Public Data ◽  
Common Purpose

Debate over the closure of DeVasco High School shows that data-driven accountability was a methodological and administrative processes that produced both transparency and opacity. Data, when applied to a system of accountability, produced new capabilities and powers, and as such were political. It created second-hand representations of important objects of analysis. Using these representations administrators spoke on behalf of the school, the student and the classroom, without having to rely on the first-person accounts of students, teachers or principals. They empowered one group—central city administrators—over another—teachers and principals. After analyzing the form these policies took, this article concludes that it is necessary to rethink the processes that create visibility and invisibility. Public data obscured the voices, experiences and collective traumas of students and faculty within the school. A narrow focus on activities within the schools rendered invisible the structural decisions made by the Department of Education in New York City—to favor small schools over large, comprehensive ones. In order to create understanding, and a sense of common purpose, those who are spoken for in simplified data must also be given the opportunity to debate the representations of their performance and quality.

A Data-Driven Approach to Prioritizing Bus Schedule Revisions at New York City Transit

2018 ◽  
Vol 2672 (8) ◽  
pp. 86-95 ◽  
Author(s):  
Malik Coleman ◽  
Lauren Tarte ◽  
Steve Chau ◽  
Brian Levine ◽  
Alla Reddy
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Data Driven ◽  
Data Driven Approach ◽  
Bus Schedule

scholarly journals The impact of COVID-19 vaccination delay: A data-driven modeling analysis for Chicago and New York City

Vaccine ◽  
2021 ◽  
Vol 39 (41) ◽  
pp. 6088-6094
Author(s):  
Vinicius V.L. Albani ◽  
Jennifer Loria ◽  
Eduardo Massad ◽  
Jorge P. Zubelli
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Data Driven ◽  
Modeling Analysis ◽  
Data Driven Modeling ◽  
The Impact

The Sustainability Myth

2020 ◽  
Author(s):  
Melissa Checker
Keyword(s):  
Climate Change ◽  
New York ◽  
New York City ◽  
Civic Engagement ◽  
Environmental Justice ◽  
Public Participation ◽  
York City ◽  
State Of The Art ◽  
Community Empowerment ◽  
Sustainable Cities

Are today’s sustainable cities built on their own undoing? This book uncovers the hidden costs of sustainable policies and practices in an era of hyper-gentrification. From state-of-the-art parks to rooftop gardens, LEED-certified buildings, bike lanes, and organic shops and restaurants, industrial waterfronts are transforming into eco-friendly urban oases. But how sustainable is this green wave? Will it lift all boats? In New York City, Melissa Checker finds that sustainable initiatives have fostered resource-intensive, high-end development in some areas and left others overburdened with polluting facilities and under-protected from climate change. Checker weaves together ethnographic and historic detail to tell the story of local activists who struggle to improve the environmental health of their neighborhoods while maintaining their affordability. For over a decade, Checker’s research on “environmental gentrification”—the use of environmental improvements to drive high-end redevelopment—has exposed the paradoxes of urban sustainability. This book develops an intricate and comprehensive account of environmental gentrification, from its historic roots to the different forms it takes. Extending this analysis, Checker also challenges popular myths about civic engagement: her work alongside environmental justice activists reveals how institutional mechanisms meant to foster public participation and community empowerment have actually undermined both. And yet Checker finds hope in surprising places. Across the country, sustainability’s broken promises have given rise to new, nonpartisan political formations. Borne of crisis, these grassroots coalitions are crossing racial, economic, and political divides to create new possibilities for our collective future.

Algorithm for Tracing Train Delays to Incident Causes

2020 ◽  
Vol 2674 (8) ◽  
pp. 264-273
Author(s):  
Anne Halvorsen ◽  
Darian Jefferson ◽  
Timon Stasko ◽  
Alla Reddy
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Data Driven ◽  
Control Center ◽  
Center Staff ◽  
Transit Networks ◽  
Root Cause ◽  
Specific Incident ◽  
In Transit

Knowledge of the root cause(s) of delays in transit networks has obvious value; it can be used to direct resources toward mitigation efforts and measure the effectiveness of those efforts. However, delays with indirect causes can be difficult to attribute, and may be assigned to broad categories that indicate “overcrowding,” incorrectly naming heavy ridership, train congestion, or both, as the cause. This paper describes a methodology to improve such incident assignments using historical train movement and incident data to determine if there is a root-cause incident responsible for the delay. It is intended as first step toward improved, data-driven delay recording to help time-strapped dispatchers investigate incident impacts. This methodology considers a train’s previous trip and when it arrived at the terminal to begin its next trip, as well as en route running times and dwell times. If the largest source of delay can be traced to a specific incident, that incident is suggested as the cause. For New York City Transit (NYCT), this methodology reassigns about 7% of trains originally without a root cause identified by dispatchers. Its results are provided to NYCT’s Rail Control Center staff via automated daily reports which, along with other improvements to delay recording procedures, has reduced these “overcrowding” categories from making up 38% of all delays in early 2018 to only 28% in 2019. The results confirm both that it is possible to improve delay cause diagnoses with algorithms and that there are delays for which both humans and algorithms find it difficult to determine a cause.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Vol 17 (9) ◽  
pp. e1009334
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

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