Multiple Areal Feature Aware Transportation Demand Prediction

TL;DR

This paper introduces a novel multi-feature-aware graph convolutional recurrent network for short-term transportation demand prediction, effectively integrating diverse areal features to improve accuracy in real-world datasets.

Contribution

The paper proposes a new spatio-temporal model that fuses multiple areal features and employs sentinel attention for improved demand prediction accuracy.

Findings

Outperforms state-of-the-art baselines by up to 8%

Effective integration of land use, sociodemographics, and POI features

Validated on real-world datasets including BusDJ and TaxiBJ

Abstract

A reliable short-term transportation demand prediction supports the authorities in improving the capability of systems by optimizing schedules, adjusting fleet sizes, and generating new transit networks. A handful of research efforts incorporate one or a few areal features while learning spatio-temporal correlation, to capture similar demand patterns between similar areas. However, urban characteristics are polymorphic, and they need to be understood by multiple areal features such as land use, sociodemographics, and place-of-interest (POI) distribution. In this paper, we propose a novel spatio-temporal multi-feature-aware graph convolutional recurrent network (ST-MFGCRN) that fuses multiple areal features during spatio-temproal understanding. Inside ST-MFGCRN, we devise sentinel attention to calculate the areal similarity matrix by allowing each area to take partial attention if the feature is not useful. We evaluate the proposed model on two real-world transportation datasets, one with our constructed BusDJ dataset and one with benchmark TaxiBJ. Results show that our model outperforms the state-of-the-art baselines up to 7\% on BusDJ and 8\% on TaxiBJ dataset.