Predicting the Place Visited of Floating Car: A Three-Layer Framework Using Spatiotemporal Probability

Abstract

Human-flow pattern can reflect the urban population mobility and the urban operating state. Understanding the trajectory of urban-population moving patterns can improve the effectiveness of urban-management measures. While most of the existing studies on human moving have placed a huge emphasis on location forecasting through the types of activities humans take part in and urban land-use types, this type of forecasting research is limited to relying on specific activity types and land-use types. The urban-population moving pattern has spatial and temporal characteristics, and this feature greatly affects the prediction of where humans will visit. This study aimed to predict the possible places to visit by using the spatiotemporal model. We analyzed the itinerary characteristics of urban taxis and proposed a model based on the taxi itinerary characteristics to predict the drop-off locations. This model can be used to predict the possible arrival locations of urban taxis. We selected three grids of travel data from each period in another day to test the prediction accuracy of the proposed model. The results show that the model can predict the destination of urban taxis to a certain degree.