[Day 163] Reading about OD demand matrix prediction models
Hello :) Today is Day 163! A quick summary of today: started concentrating more on OD demand matrix papers trying to recreate baseline models for OD demand matrix prediction Firstly, the papers I read Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction [ link ] Introduction In spatial-temporal deep learning, two main spatial data representation methods are used: image-based and graph-based. The image-based approach grids urban areas by latitude and longitude, with statistical data as pixel values for CNN models. This approach struggles with data sparsity at high granularity and loss of detail at low granularity. The graph-based approach, used for defined networks like roads, captures dynamics via GCN models but has limited structured data access and transferability. The paper proposes a method using high-granularity grids of urban areas, discarding sparse regions, and retaining significant demand signals to create virtual