Simulation Scenario Construction Method Of Social Vehicles Travelling Based On Floating Car Data

As road and infrastructures become intelligent rapidly, more and more communication equipment is installed on the vehicles and infrastructures, which makes vehicles on the road are not isolated. Consequently, these intelligent vehicles, carrying social information, are a carrier to facilitate Vehicular Social Network (VSN). With the help of these intelligent vehicles and equipment, the innovation of traffic data acquisition and process methods ensure the research of VSN. However, the lack of social vehicles travelling data blocks researching social attributes of vehicles in the VSN.Based on division of the city by urban functional areas, we propose a simulation scenario construction method of social vehicles travelling from the view of floating car data, which has advantage of wide universality. Firstly, we analyze how urban functional areas effect human mobility pattern, and excavate it by in-depth analysis of passenger travelling from floating car data. Secondly, we calcite predicted Origin-Destination Matrix of social vehicles travelling by integrating the official data with human mobility pattern. Then we use Gravity Model and Generalized Least Squares method to calibrate and correct OD Matrix, and the demand description is completed by now. Additionally, we predict urban functional areas to electric road data and complete to construct network demand after editing the electric road data. At last, we use SUMO to achieve the scenario in view of micro simulation. Finally, we generate social vehicular trajectories in morning peak, evening peak and other two periods. Besides, we generate trajectories of social vehicles on Saturday, Sunday and from Tuesday to Thursday in total of 72 hours.This thesis constructs the simulation scenario of social vehicles travelling based on floating car data and urban functional areas. We prove the superiority by comparing results with real traffic situation. Our method breaks the dependence on investigation in traditional traffic simulation, and it is a new tempt in micro simulation.