A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events

Abstract

Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n -gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.

A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events

“…Location-based models predict the location that an individual will visit [24,25], whereas trip-based models predict an individual's location in the next time interval [26,27], or simultaneously predict the departure time, the origin, and the destination of his/her next trip [28]. Although many individual mobility prediction models have been proposed, most of these models are not applicable to anomalous mobility conditions, for instance, in large crowding events [29]. Te main challenge is that individual mobility shows dramatically diferent patterns in large crowding events, and such patterns were not captured by historical data [23,30].…”