Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data
提出一种基于深度学习的城市出行预测模型，利用公交数据挖掘时空出行模式，通过卷积神经网络和长短时记忆网络结合注意力机制预测区域间客流，并融入天气等外生因素提升精度。
Timely and accurate prediction of human movement in urban areas offers instructive insights into transportation management, public safety, and location-based services, to name a few. Yet, modeling urban mobility is challenging and complex because of the spatiotemporal dynamics of movement behavior and the influence of exogenous factors such as weather, holidays, and local events. In this paper, we use bus transportation as a proxy to mine spatiotemporal travel patterns. We propose a deep-learning-based urban mobility prediction model that collectively forecasts passenger flows between pairs of city regions in an origin-destination (OD) matrix. We first process OD matrices in a convolutional neural network to capture spatial correlations. Intermediate results are reconstructed into three multivariate time series: hourly, daily, and weekly time series. Each time series is aggregated in a long short-term memory (LSTM) network with a novel attention mechanism to guide the aggregation. In addition, our model is context-aware by using contextual embeddings learned from exogenous factors. We dynamically merge results from LSTM components and context embeddings in a late fusion network to make a final prediction. The proposed model is implemented and evaluated using a large-scale transportation data set of more than 200 million bus trips with a suite of Big Data technologies developed for data processing. Through performance comparison, we show that our approach achieves sizable accuracy improvements in urban mobility prediction. Our work has major implications for efficient transportation system design and performance improvement. The proposed deep neural network structure is generally applicable for sequential graph data prediction.