摘要：

针对目前交通信息服务中出行服务方式和出行需求数据来源单一的问题,在研究交通出行个体的出行行为基础上,提出一种面向交通出行者的行为建模方法。设计基于条件约束的驻留点识别算法(SPRA),以此构建出行者移动行为模型。给出有限驻留点(FSP)聚类算法,消除因GPS误差造成的行为轨迹相异现象。设计基于兴趣点的智能语义匹配(ISM)算法,用于构建含有兴趣点的出行模式序列。通过仿真实验对上述方法的有效性进行验证,结果表明,采用SPRA算法和FSP聚类算法处理后驻留点识别精度可达90%,与基于余弦相似度的行为匹配算法相比,ISM算法的查全率更高。

关键词: 交通信息服务, 行为建模, 出行个体, GPS误差, 聚类算法

Abstract:

According to the problem that travel service mode and travel demand data sources are limited in current traffic information service, this paper proposes a method for modeling the behavior of a traveler by studying the travel behavior of individual traveler. It designs a Stay Point Recognition Algorithm(SPRA) based on conditional constraint, to construct the model of traveler’s mobile behavior. Finite Stay Point(FSP) clustering algorithm is put forward to eliminate behavior trajectory dissimilarity caused by GPS error. Intelligent Semantic Matching(ISM) algorithm based on Points of Interest(POI) is presented, which can establish a travel mode sequence containing POI. The effectiveness of the above methods is validated by using simulation experiments. Results show that the stay point recognition accuracy can reach 90% after using SPRA and FSP, while ISM algorithm has a higher recall ratio compared with the behavior recognition algorithm based on cosine similarity.

Key words: traffic information service, behavior modeling, individual traveler, GPS error, clustering algorithm

中图分类号: 

• TP18