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.

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.

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.

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.

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.