The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods; vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles.

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Development and testing of ADAS is largely based on models and simulations, but real data are indispensable for many reasons -to determine the relevant scenarios, to establish a connection between the results of the simulations and the real situation and of course as elements to set up realistic models. Using data, however, is not trivial, as not all data are informative, and even extensive data sets are often incomplete. Indeed, data is not automatically information, and the richness of the data sets is more important than their size. Data should be diverse not only with respect to different scenarios but also geographically in order to be not biased towards a specific location. In this paper, we present a new aerialview dataset of highway exits and entrances. The data have been collected in Austria and Italy and contain positions, velocities, and accelerations (in both local and global coordinate systems) of cars, vans, and trucks for Austria and additionally for motorbikes and buses in Italy. The uniqueness of the dataset consists not only in the measurements in different countries, but also in the flight height in Austria, where the recordings have been taken from 300 meters altitude allowing to observe an over 600 meters long section of the road. For non-commercial use, the dataset is available free of charge at the IEEE DataPort.