| Summary: | Connected cities use pervasive information and communication technologies, especially sensing and data analysis, to offer new decision support tools and services. One of the key use cases of connected cities is smart mobility, which addresses the use of computational tools to enhance transportation systems and private mobility. In this context, reliable information systems concerning bus arrival times provide useful services for end-users. Porto is often presented as a smart city, which has deployed a Vehicular Ad-Hoc Network with more than 600 vehicles (buses, taxis and garbage trucks) generating data regarding the GPS location of the (moving) nodes. Traces of buses location offer new possibilities to understand the city mobility patterns. The goal of this work is to develop a system for estimating bus arrival times, using Machine Learning techniques in the data available from the existing vehicular network. The developed system has three main modules: (1) line detection, responsible for inferring possible lines on which a bus may be operating; (2) machine learning model capable of predicting travel times between two bus stops and (3) service linking the current context of buses’ locations with the historical prediction model that returns predictions for a given destination stop. The prediction results obtained are in line with those reported in the literature. A proof-of-concept mobile application for the citizen was also developed, demonstrating the real-life applicability of the system.
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