Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning

The occurrence of special contexts or events (e.g., extreme weather conditions, festival events, other urban anomalies) can significantly influence the movement patterns of urban mobility (e.g., human crowds, transportation systems). Accurate mobility modeling and prediction under the occurrences of such anomaly events is therefore imperative for city management and urban resource allocation. In this study, we propose EALGAP, a novel Extreme-Aware Local-Global Attention urban mobility Prediction model. Specifically, EALGAP models the spatio-temporal global and local impacts of mobility in different regions and time steps for mobility prediction at various city regions. EALGAP takes into account the global impacts by extracting the overall or regular spatial dependencies and temporal patterns of mobility systems for different regions. We have designed a temporally-varying normalization and data-driven technique to quantify the extreme degrees, i.e., how significantly the extreme events have impacted the local mobility trend, of the patterns within different regions and time steps. We have conducted ex-tensive experimental studies upon four different mobility datasets (over 13 million trips in total) harvested from two metropolitan cities in U.S. with anomalous natural or social events (e.g., hurricane events, other extreme weather conditions, and the Federal holidays). Our results have demonstrated the accuracy, effectiveness, and extreme-awareness of our proposed EALGAP with more than 44.12% error reduction on average compared with other state-of-the-art approaches.