Abstract
Public Transportation Information Systems (PTIS) are widely used for public bus services amongst cities in the world. These systems gather information about trips, bus stops, bus speeds, ridership, etc. This massive data are an inviting source of information for machine learning predictive tools. However, it most often suffers from quality deficiencies, due to multiple data sets with multiple structures, to different infrastructures using incompatible technologies, to human errors or hardware failures. In this paper, we consider the impact of data cleansing on a classical machine-learning task: predicting urban bus commercial speed. We show that simple, transport specific business and quality rules can drastically enhance data quality, whereas more sophisticated rules may offer little improvements despite a high computational cost.
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