DIRAC: DynamIc uRban roAd traffiC noise simulation model using passive and publicly available data
Noise pollution is increasingly considered a major environmental issue in urban areas with rapid urbanization (projected to reach 68% of the World population by 2050) in the context of growing demand for mobility for people and goods in cities. It is recognized as a major cause of public health concerns e.g., linked with annoyance, sleep disturbance, and other health effects (e.g., depression, anxiety, and mood swings), and decreased productivity. In particular, road traffic is deemed to be the major source of noise in urban areas, with some 125 million people in the EU (32% of the total population) estimated to be exposed to harmful traffic noise levels.
Several initiatives have therefore followed the European Noise Directive, mandating the development of urban noise maps. However, strategic noise maps exhibit strong limitations in view of noise exposure mitigation measures: long-time (yearly) averages based on traffic flow, static representations, source-rather than receiver-centric, and non-representative of transitioning vehicle fleet. They are limited in modeling and predicting fluctuations of noise levels over time under planning and management interventions, yet being a fundamental tool to address specific dimensions of human health effects.
Built on the multidisciplinary team’s expertise and project in traffic simulation and traffic noise modeling, the DIRAC project aims to develop and demonstrate a high-fidelity road traffic noise simulation model in urban areas, empowered by ubiquitous passive traffic and open-source data and Digital Twin models. For this, data-driven models work in parallel with real-life measurements to reproduce findings and predict the results of response actions. The models are agent-based (ABM) and open-source, which can enable city stakeholders to recognize their own roles and management models for informative decision makings of noise mitigation measures toward more livable and healthy cities.
Specifically, DIRAC will model and predict microscopic and dynamic noise levels of road traffic and identifies the main noise contributors in varying traffic conditions, by integrating microscopic traffic simulation and instantaneous noise emission and propagation models. It will demonstrate, using multisource passive traffic and publicly available built environment data, a high-fidelity dynamic traffic noise simulator with geographic information system (GIS) tools to predict and visualize dynamic noise levels at a time scale and geographic granularity previously unattained.
The DIRAC project is an interdisciplinary collaboration between research teams at ABE and SCI schools, KTH, and supported by strategic research partners at KTH Center of Traffic Research (CTR, funded by Swedish Transport Administration), VTI (Swedish National Road and Transport Research Institute), Linköping University (LiU) and the University of Tartu for project references and multisource traffic data support in Stockholm. The collaboration of a fundamentally multidisciplinary nature will open a range of applications and opportunities for project funding with strong potential societal impact and transfer to industry in the emerging context of digitalized and sustainable cities.
Among the major outcomes of the proposed project, a web-interface demonstrator (with the multisource data-driven DIRAC simulator), openly available to stakeholders and the general public, will showcase the achievement of the proposed unique approach. In particular, we will demonstrate a GIS dynamic tool, which will allow us to clearly communicate the impact and potential for future applications of the dynamic noise mapping demonstrator.
PI: Zhenliang Ma, Transportation Science. Co-PI: Romain Rumpler, Technical Acoustics