[Day 194] Using Video Generation Models for Taxi OD Demand Matrix Prediction
Hello :) Today is Day 194! A quick summary of today: I finished the paper for which I read many papers and posted them throughout May/June/July Since May I have been talking about reading research related to predicting OD demand matrix using either graph neural networks or next-frame (video) prediction models. Well fast forward to today ~ and I finished it. Everything is on my github repo . I ran data through 3 models: historical average, ConvLSTM, PredRNN - HA the most common baseline, and ConvLSTM and PredRNN - two of the best next-frame prediction models. Here is the abstract of the paper: Predicting taxi demand is essential for managing urban transportation effectively. This study explores the application of next-frame prediction models—ConvLSTM and PredRNN—to forecast Origin-Destination (OD) taxi demand matrices using a concatenated dataset of NYC taxi data from early 2024. ConvLSTM achieved an RMSE of 1.27 with longer training times, while PredRNN achieved 1.59 with faster train