Taxi Demand Hotspot Forecasting with a Spatio Temporal Attention Network STAN

Описание: Taxi demand hotspot forecasting with a Spatio-Temporal Attention Network (STAN) matters because real-world mobility decisions depend on where and when demand will rise, not just the total number of trips. Operators and platforms can use these forecasts to reposition drivers in real time, anticipate which neighborhoods will spike in the next 15–60 minutes, and adjust pricing or incentives by zone. Demand patterns are driven by complex temporal effects like rush hours, day-of-week seasonality, and lagged spillovers from events. They also have spatial structure, since nearby zones influence each other through congestion, commuting flows, and short-range movement. STANs model these nonlinear spatial and temporal dependencies jointly rather than treating locations as independent or using only simple lag features. Attention mechanisms add interpretability by showing which past time steps and which neighboring zones were most influential for a given prediction. That interpretability helps analysts detect regime shifts such as weekday versus weekend behavior and explain why a specific zone became a hotspot. The economic impact is immediate for ride-hailing or taxi systems, improving driver utilization, lowering passenger wait times, and enabling surge pricing that is more anticipatory than reactive. Drivers benefit through reduced deadheading, higher earnings per hour, and less fuel and time waste. Cities benefit through better congestion management, improved curbside planning, and the ability to coordinate demand surges with public transit and street operations.