Scalable Data Analysis for Public Bus Systems

• Mayuri A. Morais UFABC
• Raphael Y. de Camargo UFABC

DOI: https://doi.org/10.5753/eradsp.2020.16885

Abstract

Urban mobility through quality public transportation is one of the major challenges for the consolidation of smart cities. Researchers developed different approaches for improving bus system reliability and information quality, including travel time prediction algorithms, network state evaluations, and bus bunching prevention strategies. The information provided by these approaches are complementary and could be aggregated for better predictions. In this work, we propose the architecture and a present a prototype implementation of a framework that enables the integration of several approaches, which we call models, into scalable and efficient composite models.

Keywords: Smart Cities, Public Bus Systems, Travel Time Prediction, Machine Learning

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