Dynamics Model Research And Empirical Analysis Of Human Mobility Behavior Based On Floating Car Data

In recent years, with the improvement of social development and human requirements for social services, the characteristics and dynamics models of residents travel behavior gradually become a hot topic. The more information on residents travel behavior we have, the better we serve for humanity life. For a long time, there are a lot of experts in this field who pay more attention to residents travel behavior because of its value in the analysis of modeling and application features.The main contents of this paper are: in the aspect of dynamic model of residents' travel behavior, we need to model for individual mobility and improve model for group mobility, then use the simulated data and empirical data to verify its accuracy respectively; in the aspect of characteristics analysis of residents' travel behavior, visualize floating car data to map and use clustering analysis algorithm to excavate Shenzhen residents hot points of traveling. In addition, we classify Shenzhen taxi passenger mode for feature extraction and mine the typical characteristics of passenger. The main contents of the paper include the following sections:As for individual traveling, we use inter-event time to derivate models of the numbers of taking a taxi within a fix time from the taxi and passengers; as for group traveling, we study different dynamic models of human behavior and choose the appropriate models to improve. This paper improves the original model of human dynamics and verify its accuracy through simulated data and empirical data which is based on the understanding of the queuing theory model of the dynamics of human behavior. The model choose individuals which have different characteristics to derive the group characteristics, but the result of the group characteristics obey power-law distribution, it shows that the characteristic of group traveling is not the superposition of individual's traveling. After the verification of the accuracy of the improved model, we make the substituted data which is based on the improved model of residents' travel behavior and verify its accuracy, then enrich the dynamic data of human behavior.As for empirical analysis, we select the appropriate map matching algorithm to visualize the floating car data in the map. Based on the statistical analysis of floating car data, we use the map visualization tools to complete the analysis in the aspect of residents' travel behavior which combined with thermodynamic maps. By comparing different clustering analysis algorithm, this paper uses hierarchical clustering analysis algorithm which was carried out on the floating car data mining. After that, we compare the results of clustering analysis with the points of interest about human traveling behaviors which come from thermodynamic diagrams to verify accuracy of thermodynamic diagrams.We conclude the taxi passenger mode of Shenzhen based on the operation area of taxi and we think the residents travel behavior in aspect of space position is subject to normal distribution, thus this paper completes the model establishment about time interval and span in space positions and characteristics analysis.