Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data

Yun Wang (), Faiz Currim () and Sudha Ram ()
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Yun Wang: Microsoft
Faiz Currim: Department of Management Information Systems, Eller College ofManagement, University of Arizona, Tucson, Arizona 85721
Sudha Ram: Department of Management Information Systems, Eller College ofManagement, University of Arizona, Tucson, Arizona 85721

Information Systems Research, 2022, vol. 33, issue 2, 579-598

Abstract: 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.

Keywords: big data; deep learning; smart transportation; predictive modeling (search for similar items in EconPapers)
Date: 2022
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