Mitigating noise and traffic congestion through measuring, mapping, and reducing noise pollution

Noise is a ubiquitous and omnipresent urban pollutant with serious health issues. This so-called “forgotten pollutant” includes all types of sounds including vehicular noise, barking dogs, music, and human sounds. Mechanical noise, and especially traffic sound, is especially “annoying” as it manifests in a plethora of ways that can be constant, intermittent, or impulsive. We have approached noise mitigation efforts via notions of “you can’t fix what you can’t measure,” “seeing is believing,” machine-aided soundscape listening, and unusually cost-effective, robust, scalable sensor network designs for dense, spatiotemporal soundmaps creation. In this paper we report on updates—Citygram’s recent partnership with IBM and its “Horizon” edge compute system, plug-and-sense sensor network hardware/software, visualizations, machine learning and sensor scaling/deployment strategies—applicable towards capturing road, rail, aircraft, and other types of urban noise agents to improve understanding of urban livability and traffic congestion. This will include discussion of NYCDOT’s noise monitoring strategies to support the NYC Off Hour Delivery Program. We believe that truck delivery-based noise can be significantly mitigated in order make urban, off-hour truck delivery practicable: a key factor in reducing congestion during the daytime and improving safety for pedestrians and cyclists during busy daytime hours in the urban environment.