This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent’s individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure ”footprint” resulting from an agent’s transportation activities.

Road traffic noise is one of the major sources of environmental noise and has been shown to increase risk of adverse health effects. For this reason, noise mapping is an efficient tool to assess and monitor city-wide noise levels in accordance with the Environmental Noise Directive (END). To relate these noise levels to the associated health effects, the noise map may be further analysed using exposure-response models, or with cost models that calculate the noise-related externalities of the traffic. With the use of microscopic traffic simulations, this assessment may be performed at a vehicle-specific level. A recently introduced methodology, referred to as the noise exposure cost, calculates the vehicle-specific contribution to the long-term social cost estimated at a macro-level based on the vehicle-specific noise contributions. However, the noise exposure cost methodology may not reflect the relative noise impact of individual vehicles, but rather solely reflect the noise contribution regardless of its relative impact to the noise levels at the time. The aim of this study is to investigate the possibility to use the linear sensitivity indicator as a basis for a non-linear weighting function in the noise exposure cost methodology, to allow the vehicle-specific noise exposure cost to better reflect the relative impact.

Noise pollution is a major health and well-being concern, being associated with, e.g., sleep disturbance, annoyance, and cardiovascular diseases. Road traffic is, by far, the largest contributor to this noise pollution in urban areas. With the growing electrification of cars in developed countries, it is commonly believed that road traffic noise pollution and the associated problems will disappear shortly in urban areas. However, at high speeds, the difference in noise emissions between internal-combustion-engine and electric vehicles is not significant, and at low speeds, the propulsion noise of electric cars is not negligible. Few works have quantitatively estimated the actual impact of passenger cars electrification on population exposure to noise, especially considering such aspects as the gradual transition towards a fully electric fleet. The present contribution thus aims to assess the evolution of population exposure to noise pollution, using a microscopic traffic simulation on the scale of an entire city, noise mapping, and various indicators, at multiple stages of this transition.

Modelling the noise exposure for a given population is essential in order to assess its potential environmental health issues and to guide authorities in their urban planning or transportation policies. This paper introduces a new method for evaluating noise exposure of large-scale populations based on MATSim, an open- source agent-based transportation simulator. A list of agents, i.e. a synthetic population, is generated using available census and travel surveys, and are used in the transport simulation. The description of the agents includes their socio-professional profile. The resulting vehicle movements are then used in NoiseModelling, an open-source CNOSSOS-EU implementation for noise emission and propagation, to compute the noise levels every time step, typically every 15mn. Agents are then followed in their daily schedule and a noise level is assigned to them every time step. The result is a 24h noise level time history for every agent. Opportunities for analysis are discussed, such as the detailed population exposure assessment and the investigation of environmental justice issues. A case study is presented and some use cases are explored. Finally, a similar coupling for air pollutants (CO2, NOx, ...) using the HBEFA emission database and the SIRANE dispersion model is under development that will enable multi-exposure assessments.

This paper proposes a methodology in traffic noise assessment, whose objective is to combine microscopic traffic simulations and noise calculation methods with macro-level, systemic noise impact assessment models. This combination, referred to as the vehicle-specific noise exposure cost (NEC), provides a per-vehicle contribution to the overall noise impact. Three case studies are introduced illustrating the potential of the methodology: a reference case with a dynamic traffic flow, the correlation between vehicle-specific NECs and average speeds, and vehicle-specific NECs in a mixed traffic fleet. The results highlight the interest and importance of using a microscopic approach, as the impact of interactions, vehicle-specific characteristics and behaviors are reflected into the associated NECs. Additionally, the correlation between vehicle-specific NECs and average speeds strongly depends on traffic conditions, further highlighting the importance of methodological features such as the interactions captured in microscopic traffic simulations or the acceleration-dependency of the implemented vehicle noise source model.

Achieving sustainable transport involves a trade-off between providing transportation while reducing traffic-related impact, such as improving resource efficiency and reducing the noise exposure. Previous studies showed that the trade-off between reducing the noise exposure cost and reducing the energy demand is dependent on other factors than the vehicle itself. Being part of traffic in a road network, the interactions between other vehicles will affect both the necessary mechanical work associated with the motion of the vehicle, the energy demand, and the resulting noise exposure cost due to the noise emissions from each vehicle in the traffic flow and the vehicles’ locations with respect to the measurement points in the network at which the noise exposure is evaluated. While initial results of microscopic traffic simulations indicate that a vehicle’s energy demand and noise exposure cost are positively correlated, it is also shown that the vehicle-specific noise exposure cost does not correlate with the increase in system-wide noise exposure costs under peak traffic conditions. For vehicles travelling in the network during peak hours, this means that a reduction in the vehicle-specific noise exposure cost may not necessarily corre- spond to a lower rate-of-change for the system-wide noise exposure cost. A different allocation strategy may improve the correlation between the vehicle-specific noise exposure cost and the system-wide effects. The study aims to analyse the correlation between different vehicle-specific effects and system-wide effects in a microscopic traffic simulation. It also aims to analyse how a weighting-based allocation strategy for the vehicle-specific noise exposure cost affects the corre- lation between the vehicle-specific noise exposure cost and the rate of change of the system-wide noise exposure cost, and how this allocation strategy in turn influences the correlation between the energy demand and the vehicle-specific noise exposure cost.

Adverse health effects related to exposure to traffic noise poses a challenge for future transport demand. To reduce the exposure of noise, and in turn reduce the health impact, the present study investigates different allocation strategies of an overall traffic noise-related cost down to individual vehicles in a microscopic traffic simulation in SUMO. Two allocation concepts were analysed. The first concept proposed cost allocation only to timesteps where the overall noise level exceeds a certain threshold. This resulted in lower cost for vehicles driving in the road network at off-peak times. The second concept introduced a non-linear scaling to shift cost either towards noisier vehicles at each timestep or toward timesteps with more noise than other timesteps. This allocation strategy showed potential to reflect the system-level effect of traffic noise to the individual vehicles in the network.

This initial study investigates the interaction between vehicle noise emissions and the energy required to move the vehicles along different drive cycles. There is a trade-off between reducing noise emissions and at the same time reducing other environmental impacts. A vehicle's energy demand associated with a specific drive cycle may be affected when a different route is chosen between two locations to reduce the noise exposure at certain observer points. The methodology of the study was to use the existing IMAGINE traffic noise model as a source model, and to evaluate the sound exposure level (SEL) at observer points as a function of instantaneous sound pressure level estimates as the source moved from one location to another via two different routes. A noise impact estimate with a linear dependence on the difference between the SEL and a threshold level was proposed. Also, the energy demand for each route was calculated. The results indicated that there is a difference between the two routes if the aim is to reduce the noise exposure or the energy demand. Suggested future research is to further improve the noise impact evaluations in the context of very short durations of exposure.