In light of the growing concern about the adverse effects of noise pollution on health, a better understanding is needed of the relationships between urban transport and individual exposure. To improve the scientific community's modeling capabilities specific to this issue, we are proposing a noise exposure modeling framework that uses agent-based activity, multi-agent travel simulation and a European standardized noise emission and propagation model. Based on two open source software packages, MATSim and NoiseModelling, this framework aims to simulate the spatiotemporal distributions of daily individual activity and road traffic noise. The proposed approach makes it possible to use all the tools and methods proposed in the NoiseModelling software by importing MATSim outputs, therefore, taking full advantage of the development work carried out within the two communities. As such, it enables both characterizing the individual exposure to road traffic-related noise and investigating noise exposure inequality problems based on the attributes of individuals and their activities.
In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique—an artificial neural network in this work—is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2–3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.
In order to more accurately estimate health outcomes related to environmental noise exposure such as sleep disturbance and noise annoyance, indicators beyond long-term equivalent sound pressure levels might be needed (such as statistical levels, number of events, psycho-acoustical indices etc). In urban noise mapping, predicting these more advanced noise indicators is especially challenging. In the current work, an open source noise mapping code (NoiseModelling) is combined with micro-traffic simulations. However, in most cities, traffic data availability is poor, especially in low traffic streets. To overcome this issue, the noise mapping procedure developed here assumes no access at all to traffic information and fully relies on Open Street Map street categorization. These street categorizations were then assigned sets of plausible traffic compositions, counts and speeds; various scenarios were explicitly simulated. In a next step, these traffic scenarios were weighted to best fit a set of 29 noise indicators on 23 measurement stations deployed in the city of Barcelona, during various periods of the day. It was shown that this procedure leads to adequate assessments of a wide range of noise indicators in a specific city.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.