Markovian city-scale modelling and mitigation of micro-particles from tires

Abstract

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.

CRAWLING: a crowdsourcing algorithm on wheels for smart parking

“…While CRAWLING can be tailored towards generic routing problems, for concreteness we focus here on the problem of routing cars to find parking spaces. Smart parking allocation is attracting much research attention 9 – 12 , as roaming for parking is known to be a major source of pollution, traffic and user stress 13 – 15 . In a broader context, lack of coordination in transportation systems is believed to be correlated to an increase in circulating vehicles 16 and reducing parking-related traffic is a question of interest in this context 17 .…”

Kemeny's constant for a graph with bridges

“…Due to its interpretation in terms of how 'well-mixing' a Markov chain is, it is not surprising that Kemeny's constant has been recently used for a wide range of applications. Some significant examples include the modeling of road traffic networks [8,23]; city-scale models of pollution from tyre particles [24]; vaccination and testing strategies in epidemiological networks [26]; and automated stochastic surveillance strategies [21].…”