Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Field trial of Tiramisu: crowd-sourcing bus arrival times to spur co-design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Robust Gaussian Process Regression with a Student-t Likelihood
The Journal of Machine Learning Research
Pervasive Technology and Public Transport: Opportunities Beyond Telematics
IEEE Pervasive Computing
Measuring Public-Transport Accessibility Using Pervasive Mobility Data
IEEE Pervasive Computing
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The dynamics of a city are characterized, among others, by the traveling patterns of its dwellers. Accurate knowledge of human mobility patterns would have applications, e.g., in urban design, in the optimization of public transportation operating costs, and in the improvement of public transportation services. The present paper combines a large scale bus transportation dataset with publicly available data sources to predict bus usage. We propose a Gaussian process-based approach for modeling and predicting bus ridership. To validate our approach we perform experiments on data collected from Lisbon, Portugal. The results demonstrate significant improvements in prediction accuracy compared to a probabilistic baseline predictor.