Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Understand

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period.

• Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets.
• Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data.
• However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time.