基于出行模式的公交IC卡乘客下车站点判断方法

摘要

Analyzing and mining the data of Advance Public Transportation Systems can provide scientific decisionmaking basis for urban public transportation planning and management.In flat-rate automated fare collection system,alighting stops information of the passengers is not available directly.The existing method can only determine part of alighting stops,which is a limiting factor for the application of smart card data.According to time-spatial regularity of transit travel,this paper presents the method of estimating alighting stops based on commuting travel patterns and related travel patterns.Commuting OD is judged by commuting regularity,and then the alighting stops of commuting trips are estimated on the basis of commuters' residences and work places.Based on long-term time-spatial correlation of travels,alighting stops are estimated by matching the related travels.The algorithm was implemented,and the experiment and accuracy analysis were carried out using large-scale actual data.The results show that the method can help improve the ratio of estimating alighting stops effectively,and the accuracy ratio reached 87.90％.This can be used in practical work of public transportation planning and management.%对智能公交系统数据进行分析和挖掘,能够为城市公共交通系统的规划和管理提供科学的决策依据.一票制的公交IC卡收费系统仅在上车时刷卡,乘客下车站点的信息无法直接得到,而现有方法只能判断出部分乘车记录的下车站点,这成为公交IC卡数据应用的制约因素.根据公交乘客出行的时空规律性,提出基于通勤出行模式和关联出行模式判断下车站点的思路.利用通勤出行规律判断公交乘客的通勤OD,再根据其居住地和工作地站点信息对通勤出行的下车站点进行估计.依据公交乘客长期出行具有一定时空关联性的规律,通过将当日出行与他日的关联出行数据进行匹配来判断下车站点.对提出的方法进行算法实现,并应用大规模实际数据对算法进行了实验和精度分析.研究结果表明:该方法能够有效提高下车站点的判断比例,准确率达到87.90％,能够应用于实际的公交系统规划和管理工作.