详情

原文传递 Classification of Livebus arrivals user behavior

题名：	Classification of Livebus arrivals user behavior
其他题名：	Ahas,R.,Aasa,A.,Silm,S.&Tiru,M.(2010).Daily rhythms of suburban commuters movements in the tallinn metropolitan area:Case study with mobile positioning data.Transportation Research Part C:Emerging Technologies,18(1),45–54.
正文语种：	英文
作者：	Natalia Selini Hadjidimitriou
关键词：	Clustering algorithms;data-driven behavior;data mining;intelligent transport system;public transport;real time information;supervised classification
摘要：	With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for realtime arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support VectorMachine (SVM) by SequentialMinimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classificationbased approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMOmethodology (Precision of 97％). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37％ of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could supportmanagement and planning by revealing how public transport and related services are actually used or update information on commuters.
出版年：	2017
论文唯一标识：	J-96Y2017V21N05003
英文栏目名称：	Articles
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
拼音刊名(出版物代码)：	J-96
卷：	21
期：	05
页码：	375-389