车联网环境下基于拉格朗日坐标系的交通流模型研究

项目名称： 车联网环境下基于拉格朗日坐标系的交通流模型研究

项目编号： No.51208101

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 建筑环境与结构工程学科

项目作者： 王卫

作者单位： 东南大学

项目金额： 25万元

中文摘要： 车联网环境下RFID、手机、GPS等交通信息采集技术无法实现对交通流量、密度和速度三参数的同步检测，导致传统交通流模型在交通信息处理、交通流状态检测等方面因不能满足三参数的输入要求而无法直接应用。为解决上述问题，将车联网多源交通信息采集方法归纳为地点检测法、匹配检测法和轨迹检测法三类，分别研究这三类检测方法所得数据各自遵循的车辆守恒、时间守恒和空间守恒定律；采用上述三类守恒定律在拉格朗日坐标系下独立进行交通流建模并设计出相应的高效数值解法；构建开发试验平台并进行模型的数据验证。预期成果可以为车联网多源交通流检测数据提供有效处理方法，获得随样本数调整弹性分辨率的动态高精度交通流状态信息，为宏观交通流和微观个体车辆定制化信息服务与管理控制提供数据和模型支持，有助于完善车联网发展的理论体系。

中文关键词： 车联网；拉格朗日坐标；交通流模型；交通流状态估计；有限差分

英文摘要： Traffic flow characters detection methods like RFID, Cellular phone,GPS can not provide traffic flow volume, density and spped information at one time. Because of the insufficient parameter input, traditional traffic flow models can not be applied directly for connected vehicle applications such as traffic information processing, traffic flow state detection, etc. traffic information detection methods like Inductive loop, RFID, cellular phone, GPS are classified in to three categories, which are point detection method, paired detection method and trajectory detection method. These three categories methods should follow vehicle conservation law, temporal conservation law and spatial conservation law separately. New traffic flow models are built according to these three conservation laws above in Lagrangian coordinates, and efficient numerical solution design is followed. Finally, experimental environment is formed using real data for model testing and analysis. The expected achievements can provide methods for multiple source traffic detection data procession, result in high accuracy and variable resolution traffic flow state information, support the customized information, operation and control service for macro traffic flow and individual connected vehicles with traffic flow state data and models. The research

英文关键词： connected vehicle；Lagrangian coordinates；traffic flow modeling；traffic state estimation；finite difference