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Estimating the Social Gap With a Game Theory Model of Lane Changing
Published 2020-05-01
Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. ...
Open Access
Preprint
How Transit Scaling Shapes Cities
Published 2019-11-13
Transit accessibility to jobs (the ease of reaching work opportunities with public transport) affects both residential location and commute mode choice, resulting in gradations of residential land use intensity and transit ...
Open Access
Preprint
Unifying Access
Published 2020-05-06
Since Hansen’s seminal 1959 paper, measures of access have evolved with technology and through practical applications. The myriad of distinct but familially related access measures created many more options to measure ...
Open Access
Preprint
Catchment if you can: The effect of station entrance and exit locations on accessibility (journal preprint)
Published 2019-10-02
The success of passenger railway systems depends on their ridership and thus the population they serve. A mechanism to increase ridership is to expose the existing system to more people by reconfiguring the station itself, ...
Open Access
Preprint
Commute Mode Share and Access to Jobs across US Metropolitan Areas
Published 2019-10-03
How much of the variation in transit mode share is attributable to accessibility is not well understood, despite its significant policy implications. It is hypothesized that better transit accessibility leads to higher ...
Open Access
Preprint
Job-worker spatial dynamics in Beijing: Insights from Smart Card Data.
Published 2019-10-02
As a megacity, Beijing has experienced traffic congestion, unaffordable housing issues and jobs-housing im- balance. Recent decades have seen policies and projects aiming at decentralizing urban structure and job-worker ...
Open Access
Preprint
Predicting the Risk of Clinical Deterioration in Patients with Severe COVID-19 Infection Using Machine Learning
Published 2020
Background: Only part of the severe patients with coronavirus disease 2019 (COVID-19) may deteriorate to become critically ill and require aggressive treatments and intensive care. We aim to develop an early-warning system ...
Preprint
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
Published 2020
A recent model developed at the Institute for Health Metrics and Evaluation (IHME) provides forecasts for ventilator use and hospital beds required for the care of COVID19 patients on a state-by-state basis throughout the ...
Preprint
Modelling transmission and control of the COVID-19 pandemic in Australia
Published 2020
In this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to reproduce several characteristics of COVID19 transmission, ...
Preprint
Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity
Published 2021
ABSTRACT Good vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim ...
Preprint