Multiple Areal Feature Aware Transportation Demand Prediction

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.

ForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting Questions

Textual data are often accompanied by time information (e.g., dates in news articles), but the information is easily overlooked on existing question answering datasets. In this paper, we introduce ForecastQA, a new open-domain question answering dataset consisting of 10k questions which requires temporal reasoning. ForecastQA is collected via a crowdsourcing effort based on news articles, where workers were asked to come up with yes-no or multiple-choice questions. We also present baseline models for our dataset, which is based on a pre-trained language model. In our study, our baseline model achieves 61.6% accuracy on the ForecastQA dataset. We expect that our new data will support future research efforts. Our data and code are publicly available at https://inklab.usc.edu/ForecastQA/.