[Day 154] Diving deeper into Graph Neural Networks used in taxi demand prediction
Hello :) Today is Day 154! A quick summary of today: STGCN - Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting CACRNN - Predicting taxi demands via an attention-based convolutional recurrent neural network Thanks to a lab mate, I found a way to sync my Obsidian notes using google drive. So I don't need to use github anymore! But still I have to upload text with math notations as pictures. Frist paper, STGCN Introduction The paper presents methods to effectively capture the temporal and spatial patterns in traffic flow. Instead of viewing the traffic network as separate grids or segments, it represents it as a general graph to better leverage spatial data. To address the shortcomings of recurrent networks, a fully convolutional structure along the time axis is used. The key contribution is the development of a novel deep learning model, called spatio-temporal graph convolutional networks, specifically designed for traffic forecastin