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Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data

机译：使用自动票价集数据推断在多模态旅行环境中的原始目的地需求和用户偏好

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摘要

Eliciting individual travelers & rsquo; Origin-Destination (OD) information is critical for enabling public transit system policy-makers and operators to serve travelers in a calculated way. Accurate estimation of route choice model parameters is also important, in that it can help assess or predict the service levels that such a system can be expected to achieve. The knowledge of both the OD links and route choice logic is especially in demand for emerging mobility services, where providers work to accommodate individu-alized services and also offer incentives to travelers for specific trips. We show that all this information can be distilled from a particular type of data & ndash; the Automated Fare Collection (AFC) system data & ndash; in a fast, low-cost way. This paper presents a two-step methodological framework to identify individual trav -elers & rsquo; true ODs (beyond stop-level ODs), as well as infer their travel preferences. The key to our work is the ability to identify and process the observations of travelers & rsquo; routing choices between the same ODs under different travel environment conditions. A presented specially-crafted case study validates the pro-posed method in application with a real-world AFC data of Seoul, Korea, confirming the method & rsquo;s high inferential ability, under a basic route choice model. (c) 2020 Elsevier Ltd. All rights reserved.
机译：引出个人旅行者和rsquo;原始目的地（OD）信息对于使公共交通系统政策制定者和运营商以计算的方式为旅行者提供服务至关重要。准确估计路由选择模型参数也很重要，因为它可以帮助评估或预测可以预期这种系统实现的服务水平。对OD链路和路线选择逻辑的知识尤其是对新出现的移动服务的需求，提供商工作，以适应个人化的服务，并为特定旅行提供给旅行者的激励措施。我们表明，所有这些信息都可以从特定类型的数据和ndash蒸馏出来;自动票价收集（AFC）系统数据和Ndash;以快速，低成本的方式。本文介绍了两步的方法框架，以识别个人TRAV -ELER和RSQUO;真正的ods（超越停止级别的ods），以及推断他们的旅行偏好。我们工作的关键是能够识别和处理旅行者的观察和rsquo;在不同旅行环境条件下的同一ODS之间的路由选择。呈现的特制案例研究验证了在基本路由选择模型中确认了韩国的真实AFC数据，验证了应用中的Pro-Posed方法。 （c）2020 elestvier有限公司保留所有权利。

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