# A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

## TL;DR

This paper introduces STEF-Net, a novel deep neural network that combines convolutional LSTM and fuzzy neural networks to improve passenger demand prediction by modeling complex spatial-temporal interactions and external uncertainties.

## Contribution

It is the first to fuse deep recurrent and fuzzy neural networks for modeling complex spatial-temporal features with uncertain external factors in demand prediction.

## Key findings

- Achieves over 10% improvement over state-of-the-art methods.
- Effectively models external uncertainties with fuzzy neural networks.
- Captures complex spatio-temporal interactions using convolutional LSTM.

## Abstract

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.