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Mining Combined Smart Card Data and Survey Data for Transit Users' Travel Patterns and Demands

机译：挖掘结合智能卡数据和调查数据的过境用户的出行方式和需求

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摘要

A better understanding of travel patterns and demands of transit users helps to evaluate current transit services and improve transit service. However, it's challenging to identify passengers' long-term travel characteristics and demands simultaneously through a single dataset. This paper investigates the classification of transit users and interprets passenger segmentation through the analysis of travel patterns and demands using combined smart card data and survey data. Transit users were grouped into segments based on the travel patterns revealed in smart card data, and the users' demands were subsequently analyzed using survey data to improve the understanding of demands on the transit system. The finding indicate that the clustering method based on travel patterns work well to distinguish the transit users and their demands. The segmentation of users enables authorities to precisely reach users with similar demands. The paper' method and conclusion could support more precise operational and strategic decisions.
机译：更好地了解过境模式和过境用户的需求有助于评估当前的过境服务并改善过境服务。但是，通过单个数据集同时识别乘客的长期旅行特征和需求是一项挑战。本文研究了过境用户的分类，并通过结合使用智能卡数据和调查数据的出行方式和需求分析来解释乘客的细分。根据智能卡数据中显示的出行方式将过境用户分组，然后使用调查数据对用户的需求进行分析，以提高对过境系统需求的理解。研究结果表明，基于出行方式的聚类方法可以很好地区分公交用户及其需求。用户的细分使权限可以精确地到达具有类似需求的用户。本文的方法和结论可以支持更精确的运营和战略决策。

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