Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data
Lorenz Cuno Klopfenstein, Saverio Delpriori, Paolo Polidori, Andrea Sergiacomi, Marina Marcozzi, Donna Boardman, Peter Parfitt, Alessandro Bogliolo · International Review of Applied Economics · 2019
This paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the participation of engaged citizens into communities that contribute towards a shared goal. In providing people with the tools needed to positively impact society, crowd-based initiatives can be seen as purposeful drivers of social innovation from the bottom. Mobile crowdsensing (MCS), in particular, takes advantage of the ubiquitous nature of mobile devices with on-board sensors to allow large-scale inexpensive data collection campaigns. This paper illustrates MCS in the context of road surface quality monitoring, presenting results from several pilots adopting a public crowdsensing mobile application for systematic data collection. Evaluation of collected information, its quality, and its relevance to road sustainability and maintenance are discussed, in comparison to authoritative data from a variety of other sources.