A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

With the significant updates of location-acquisition technologies, there are more spatial-temporal trajectory data available. Spatial-temporal stream plays a crucial role in the research and applications of intelligent transportation. However, there are still some problems in the study of multi-target hotness diffusion in spatial-temporal stream scenarios: such as forecast of POIs' (Points of Interest) visitors or short-term traffic. Passenger and traffic flow can be called spatial-temporal hotness, the law of hotness diffusion in spatial-temporal of multi-hot-spots can be detected by studying their trajectory. Spatial-temporal trajectory data often presents multi-core (or multi-target) characteristics around events or behaviors, it is still a challenge to carry out multi-target modeling at the spatial-temporal level. Here, we provide a compelling method for dealing with multi-target hotness diffusion. To excavate the hotness in the movement and diffusion laws in spatial-temporal, we treat movements as a long network infrastructure flood from its source. Through modeling and analysis of OD (Origin-Destination) stream, the hotness prediction is finally achieved. Finally, two groups of experiments were used to demonstrate our method from the perspectives of passenger flow and traffic flow respectively and the experiments based on real-world data show that the effectiveness of our method in predicting the spatial diffusion state of multi-target hotness in different spatial scales. Based on the R-square, MAE (Mean Absolute Error), MSE (Mean Squared Error), and other evaluation indexes compared to the traditional prediction method and ARIMA (Autoregressive Integrated Moving Average) model. Thus, these findings suggest that our method shows more advantages than others.