A Study of Population Area Flows Based on Spatio-Temporal Big Data from the Internet of Vehicles (IoV)

This paper studies the population area flow in different areas based on the spatio-temporal big data from IoV in that area. Firstly, the related spatio-temporal big data are collected through the IoV communication technology of mobile network and interconnected mobile ecosystem technology, and the data fields are optimized by using the feature engineering technology to obtain the effective data fields; secondly, the latitude and longitude data are mapped into the two-dimensional map data through the HMM algorithm and the road network fitting technology; then, the parsed data are subjected to the density-based clustering modeling, and the study of the population area flow over time is carried out. Finally, five cities are selected to verify and analyze the results of this paper, which show that the model can analyze the human flow travel with the help of vehicle spatio-temporal data, and can accurately grasp the distribution and flow pattern of the human flow in different regions and time periods, which can provide data support for the development and planning of the city.