基于海量语义轨迹的交通驾驶行为认知与主动学习机理研究

项目名称： 基于海量语义轨迹的交通驾驶行为认知与主动学习机理研究

项目编号： No.61304199

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 廖律超

作者单位： 福建工程学院

项目金额： 26万元

中文摘要： 如何从海量的浮动车轨迹数据中有效挖掘驾驶行为知识，进而为公众提供智能友好的主动交通信息服务一直是国内外在智能交通诱导、交通安全管理等方面的研究热点和难题。本课题拟深入分析海量浮动车轨迹数据的时空特性，并重点研究多信息粒度的语义轨迹转换模型、驾驶行为度量四维模型及区域带路专家发现模型，以探索基于海量语义轨迹的驾驶行为挖掘认知与主动学习机理。其中，多信息粒度的语义轨迹转换模型将有效增强轨迹数据的语义信息；驾驶行为度量四维模型将融合语义轨迹数据中的空间特性、时间特性、速度特性与方向特性，提取用户宏观与微观的驾驶行为特征，以表征用户的驾驶行为特性；而区域带路专家发现模型则提供基于海量语义轨迹数据的驾驶行为经验知识提炼及其主动学习机制。同时，本课题将基于MapReduce机制进行模型的并行处理优化，以满足主动交通信息服务的时效性要求，并构建海量语义轨迹数据资源开放共享平台。

中文关键词： 轨迹数据；大数据；数据挖掘；潜在语义分析；驾驶行为

英文摘要： Ming the knowledge on driving behavior from the massive FCD (Floating Car Data) and providing the traffic information service for public with intelligent and friendly interface, is a hot but difficult topic, in the fields of traffic guidance and traffic safety management. With the deep analysis on the spatial and temporal characteristics of the trajectories of the massive float cars, the projection will focus on the multi-granularity semantic trajectory conversion model, four-dimensional driving behavior measure model and regional leading experts found model, to explore the driving behaviors cognition and its active learning based on massive semantic trajectories. The first part, the multi-granularity semantic trajectory conversion model will aim to enhance the semantic information of trajectory data effectively. The second one, the four-dimensional driving behavior measure model will integrate the spatial characteristics, time characteristics,speed characteristics and direction characteristics of semantic trajectory data, and then extract the users' macroscopic and microscopic driving behavior characteristics to depict their driving behavior characterizations. Also, the regional leading experts found model will be consider to meet driving behavior knowledge extraction and active learning demand based on mass se

英文关键词： trajectory data；big data；data mining；latent semantic analysis；driver behavior