Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Abstract

Urban mobility presents various challenges to favor urban development. These challenges have been traditionally analyzed using transport network optimization and simulation techniques. Nevertheless, it is possible to think of process mining as a complementary approach allowing, among other things, to discover behavioral transportation models, obtain execution measures and detect bottlenecks. The objective of this article is to analyze how suitable PM is for the analysis of urban mobility problems. We use open data from the Metropolitan Transportation System (STM) of Montevideo, Uruguay, which, among other things, provides the ability to record up-to-date information on its transportation network and trips of its citizens. We apply process mining to process discovery, both from buses and their users, and carry out various analyses linking such data with time information, costs, types of users, and city areas.