Traffic noise feedback in agent-based Integrated Land-Use/Transport Models

Abstract

Road traffic is a common source of negative environmental externalities such as noise and air pollution. While existing transport models are capable of accurately representing environmental stressors of road traffic, this is less true for integrated land-use/transport models. So-called land-use-transport-environment models aim to integrate environmental impacts. However, the environmental implications are often analyzed as an output of the model only, even though research suggests that the environment itself can have an impact on land use. The few existing models that actually introduce a feedback between land-use and environment fall back on aggregated zonal values. This paper presents a proof of concept for an integrated, microscopic and agent-based approach for a feedback loop between transport-related noise emissions and land-use. The results show that the microscopic link between the submodels is operational and fine-grained analysis by different types of agents is possible. It is shown that high-income households react differently to noise exposure when compared low-income households. The presented approach opens new possibilities for analyzing and understanding noise abatement policies as well as issues of environmental equity. The methodology can be transferred to include air pollutant emissions in the future.

FABILUT: The Flexible Agent-Based Integrated Land Use/Transport Model

“…To do so, demographic events like birth, marriage, and death, household relocation as well as real-estate updates like construction, renovation, and price changes are explicitly simulated by corresponding submodels. In the latest version of SILO, each dwelling and job is geo-referenced to a microscopic coordinate (Kuehnel et al 2021). Also, SILO models work start times for each job explicitly.…”

“…Analysis of environmental impacts A high spatial resolution is a key property of an urban model to allow for analyses of environmental aspects (Kuehnel et al 2020;Spiekermann and Wegener 2008). Noise, for instance, varies on a microscopic scale, especially when the shielding by buildings is taken into account (Kuehnel et al 2021).…”

“…In SILO's latest version, each dwelling and job is geo-referenced to a microscopic coordinate which makes the model suitable for fine-grained environmental assessments like noise impact analyses (Kuehnel et al 2021). MATSim is fully coordinate-based and possesses extensions to model noise (Kaddoura et al 2017) and emissions (Kickhöfer and Kern 2015).…”

The uneven geography of the accessibility and environmental quality in the global north and south: Introduction to the special issue

“…Firstly, modeling the environmental effects of land use and transport in existing land use-transport models is very limited (Acheampong and Silva, 2015;Tayarani and Rowangould, 2020;Tayarani et al, 2018). Some pioneering work has been done in integrating environmental aspects in urban models (e.g., Kuehnel et al, 2021) however the impact of environmental and health variables on land-use and transport decisions is often ignored or modelled at the aggregated zone level. Kuehnel et al (2021) presents a proof of concept for an integrated, microscopic and agent-based approach for a feedback loop between transport related noise emissions and land-use, and show that high-income households tend to relocate to more quiet dwellings compared to low-income households relocating to lower cost housing with high noise exposure.…”

“…Some pioneering work has been done in integrating environmental aspects in urban models (e.g., Kuehnel et al, 2021) however the impact of environmental and health variables on land-use and transport decisions is often ignored or modelled at the aggregated zone level. Kuehnel et al (2021) presents a proof of concept for an integrated, microscopic and agent-based approach for a feedback loop between transport related noise emissions and land-use, and show that high-income households tend to relocate to more quiet dwellings compared to low-income households relocating to lower cost housing with high noise exposure. Secondly, the ability to integrate transport equity into standard modeling feedback mechanisms is simply beyond the current generation of LUTI models, and as van Wee (2015) suggests, it is unlikely the next generation LUTI models will be capable of providing such outputs with any degree of confidence.…”