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Grids-versus-Graphs

The widely employed ConvLSTM for modeling spatio-temporal data can model only data in fixed-sized partitions. The standard convolutional filters cannot be applied on variable-sized partitions. The search for a model that works well with irregular spaced partitions made us delve into Graph-based LSTM models. A comparison of ConvLSTM and GraphLSTM reveals the competitive performance of GraphLSTM, at a lower computational complexity, across three real-world large-scale taxi demand-supply data sets, with different performance metrics.

For graph-based modeling, we borrow heavily from Cui, Zhiyong, et al. "Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting." and their code at https://github.com/zhiyongc/Graph_Convolutional_LSTM

For ConvLSTM and LSTM baselines, we use the standard Keras libraries.