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Improvements in noise mapping techniques and smart cities infrastructure have fostered the development of new ways to evaluate traffic noise exposure. An example of one such outcome is the noise exposure sensitivity map, which quantifies the noise exposure potential of a road network as a function of the vehicle type, the prevailing background noise, and the population exposed. The potential for planning vehicle routing that is offered by the above-mentioned map calls for revisiting the Vehicle Routing Problem (VRP) with a focus on accounting for the noise exposure in the optimization process. A case study is chosen for applying the VRP, and it is taken from last-mile off-peak deliveries performed in Stockholm, Sweden, in the context of the CIVITAS Eccentric project. The VRP is independently solved for the following objectives: distance travelled, driving time, and driving noise exposure potential. Also considered is a heterogeneous objective that is consisting of a combination of these factors. The impact of the objective function on the resulting routes is presented. A sensitivity analysis is performed to determine the trade-offs between the chosen factors.

State-of-the-art urban road traffic noise propagation simulation methods such as the CNOSSOS-EU framework rely on ray tracing to estimate noise levels at specific locations on façades, so-called receiver points; this method is computationally expensive and its cost increases with the number of receiver points, which limits the spatial accuracy of such simulations in the context of real-time or near-real-time urban noise simulation applications. This contribution aims to investigate the applicability of multiple data-driven methods to the surrogate modelling of traffic noise propagation for fast façade noise calculation as an alternative to these traditional, ray-tracing-based methods. The proposed approach uses compressed sensing to select a small subset of receiver points from which the data set of the entire façade may be reconstructed, associated with a Kriging model and neural networks, used to predict noise levels for these sensors. The prediction performance of each of these steps is evaluated on an academic test case, with two levels of complexity based on the dimensionality of the problem.