Analysis and comparison of publicly available databases for urban mobility applications

Abstract

The challenges of multimodal applications can be addressed with machine learning or artificial intelligence methods, for which a database with large amounts of good quality data and ground truth is crucial. Since generating and publishing such a database is a challenging endeavour, there are only a handful of them available for the community to be used. In this article, we want to analyze three of these databases and compare them. We assess these databases regarding the ground truth that they provide, e.g. labels of the means of transport, and assess how much unlabelled data they publish. We compare these databases regarding the number of hours of data, and how these hours are distributed among different means of transport and activities. Finally, we assess the data in each public database regarding crucial aspects such as the stability of the sampling frequency, the minimum sampling frequency required to observe certain means of transport or activities and, how much lost data these databases have. One of our main conclusions is that accurately labelling data and ensuring a stable sampling frequency are two of the biggest challenges to be addressed when generating a public database for urban mobility.