Localized polygon-based models trained on clustered bus stops achieve prediction accuracy comparable to a single global model when using ridership, spatial, weather, and temporal features.
Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time.Transportation Re- search Part B: Methodological, 46(8):1068–1084, 2012
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Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
Localized polygon-based models trained on clustered bus stops achieve prediction accuracy comparable to a single global model when using ridership, spatial, weather, and temporal features.