Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36–0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68–0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors

A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

Despite the importance of rail infrastructure to the effective and efficient functioning of dense urban areas and their commercial business districts, funding for operations and maintenance of transit systems is a common challenge for cities. Operational funds are derived from a range of sources, including fare and toll revenues, taxes, and fees. In cities with aging infrastructure, traditional funding mechanisms are falling short of actual need, even as many cities experience record ridership levels. Therefore, new funding streams are necessary to safely, efficiently, and equitably operate and maintain an aging rail infrastructure in the face of growing demand. This paper presents a socio-spatial model of rail transit ridership demand to develop a computational method for value-capture funding mechanisms that link existing commercial properties and transit infrastructure operations. Using a diverse range of large-scale data for New York City (NYC) and the surrounding region, our methodology provides a data-driven approach to address fundamental issues of horizontal and vertical equity in value-capture fees, including (1) the magnitude of the special assessment, (2) the property types to include, and (3) the boundaries of the special assessment district. We find that a marginal special assessment of $0.50 to$1.00 per square foot on commercial properties, proportionate to the lost wages and output associated with system delays, within 1/4-mile of a subway station in NYC\u27s core commercial district could yield between $332 and$664 million annually to support the Metropolitan Transit Authority\u27s operating budget. This is equivalent to the revenue generated by an average, system-wide per ride fare increase of $0.22, and significantly less than the estimated implicit transit subsidy for these buildings of$4.58 per square foot per year

Working 9 to 5? Measuring Hyperlocal Worker Productivity with Public Wifi Network Data

69A3551747124The research reported in this pre-print was funded by the US DOT University Transportation Centers Program.This content is held in a repository external to US DOT and NTL, but has been made available by the authors with the following caveat on the first page of the pre-print: Draft \u2013 Not for Citation Without Permission of the Authors.The accurate estimation of workday length is essential to estimate total labor supply, and has a significant bearing on the assessment of labor productivity and worker well-being. Using probe request data from a 53 access-point, publicly-accessible Wi-Fi network in the Lower Manhattan district of New York City, we develop a method to measure localized worker activity patterns. Our Wi-Fi network data consist of over 10,000,000 probe requests per day, accounting for approximately 9.5 million unique devices over the study period from April 2017 to September 2017. We describe worker activity at various spatial and temporal aggregations in order to define baseline workday patterns and compute the workday length. We find a substantial population with characteristic workday lengths (e.g. 9am-5pm) during the workdays, as well as diurnal activity patterns that are consistent with expected worker behavior. These temporal patterns provide sufficient evidence to reinforce our assumptions about the ability to identify worker populations from Wi-Fi data. Finally, we compute the workday length for each identified worker and aggregate these workday lengths to estimate collective workday patterns to understand the uniformity of worker behavior. We find workday lengths of 7 hours and 40 minutes on average, which shorten substantially on Fridays and days surrounding holidays. We also find considerable seasonal variation in total workday hours supplied in our study area. This dynamic pattern of hours-worked suggests that our methodology is able to accurately assess workday lengths at high spatial resolution and temporal frequency. The ability to quantify hyperlocal worker activity patterns has a broad range of applications, including estimates of localized economic output and changes in labor supply