Intelligent Data Metrics for Urban Driving with Data Fusion and Distributed Machine Learning

Abstract

Using a community of users allows the collection of data that can be used towards the benefit of society. Aligned with trends such as smart city design and the internet of things, the range of application are being only restricted by human imagination. Taking the case of urban driving, it is already possible to estimate roadblocks, congestions and issue real-time alerts to users using popular applications. The approach taken in this paper, furthers this analysis by providing means to analyse the root cause of riot only such events but also dangerous driving habits from users. Making use of machine learning algorithms, big data and distributed systems, a work-flow based on the PRESS Driving platform was developed. Results achieved are satisfactory in the field tests produced, giving reason to some popular common sense, as well as, new theories for dangerous driving events.