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On the impact of noise and energy demand from traffic

An assessment using microscopic modelling

Time: Wed 2021-04-21 10.00

Location: Live-streaming via Zoom,, (English)

Subject area: Vehicle and Maritime Engineering

Doctoral student: Johan Nygren , Marcus Wallenberg Laboratoriet MWL, VinnExcellence Center for ECO2 Vehicle design

Opponent: Researcher Arnaud Can, UMRAE, Université Gustave Eiffel, IFSTTAR, CEREMA, 44340 Bouguenais, France

Supervisor: Universitetslektor Susann Boij, Marcus Wallenberg Laboratoriet MWL, VinnExcellence Center for ECO2 Vehicle design; Forskare Romain Rumpler, Marcus Wallenberg Laboratoriet MWL, VinnExcellence Center for ECO2 Vehicle design; Associate Professor Ciarán J. O'Reilly, Farkostteknik och Solidmekanik, VinnExcellence Center for ECO2 Vehicle design

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Noise emissions from transportation remain one of the greatest environmental issues of modern day. Inhabitants in urban environments are especially exposed, with almost 80 million people in the European Union exposed to noise levels exceeding the recommended limits set by the World Health Organization (WHO). While the health-related effects from exposure of traffic noise are problematic and of utmost importance to reduce, availability to efficient transport is also an essential necessity. These conflicting requirements on transportation calls for a more holistic approach to traffic analysis, and  to understand the relation between these effects from the traffic. This work investigates properties of traffic, such as the exposure of noise emissions, the vehicle-specific energy demand and duration, to analyse the sustainability of transport.

The traffic simulation software SUMO is used to provide a discrete traffic model with individual vehicles, combined with the European vehicle noise source model IMAGINE used to model discrete sound sources that allow for directivity in the sound field and is speed- as well as acceleration-dependent. The resulting cost related to the exposure of noise is then evaluated at several measurement points in the network using a willingness-to-pay (WTP) model. This allows for an analysis of the relation between noise exposure cost and energy efficiency through the estimation of the vehicle-specific energy demand. A time-varying traffic demand is added to analyse the effects of a varying traffic density and congestion to the relation between the different properties.

Additionally, the concept of allocating the noise exposure cost down to individual vehicles by means of contributed acoustic energy is expanded to take the main contributing vehicles and time-segments into consideration, and to allow for a non-linear weighting factor. These allocation strategies also allow for a bias to assign a higher cost to noisier vehicles, as vehicles that contribute more to the overall noise exposure than others may be more easily identified.

Lastly, the relation between the traffic properties are analysed by means of correlation. Initial studies indicate that the correlation is dependent on the traffic density and the amount of vehicle interaction.