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Journal Article

Citation

Peters A, Hernández D, Kioumourtzoglou M, Johnson MA, Chillrud SN, Hilpert M. Environ. Sci. Policy 2022; 133: 155-163.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.envsci.2022.03.010

PMID

35910007

PMCID

PMC9328407

Abstract

BACKGROUND: The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse.

OBJECTIVE: The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 to 2019.

METHODS: We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion.

RESULTS: In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night.

CONCLUSIONS: Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.


Language: en

Keywords

Congestion; Crowd-sensed data; Crowd-sourced data; Environmental justice community; Google traffic data; Road traffic; Traffic radar

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