For decades, transportation researchers have used survey data to understand the factors that affect travel-related choices. Nowadays, travel surveys lay the foundation of travel behavior analysis for transportation modeling, planning, and policy-making. The development of information technology for urban sensing has enabled substantial improvements to be made in survey-elicited and passive mobility data collection. Actively collected and passive data are very different, and being able to compare and integrate them could allow stakeholders to achieve a greater understanding of human mobility. The comparison between survey self-reported travel behavior and actual travel behavior revealed by urban and mobile systems provides us with the opportunity to find potential discrepancies. Previous work has examined these discrepancies mostly at the population level. An individual-level investigation of these discrepancies could provide many benefits, from increasing our understanding of survey and passive data accuracy and collection, to designing personalized transportation services. In this study, the discrepancies between self-reported and observed travel behavior are analyzed at both the individual and aggregated level by utilizing the available mobility data, namely, survey-based commuting diaries and passive mobility records. We propose a group of discrepancy metrics for commuting activities for which we have available and comparable data, and apply the framework to an empirical analysis at the Massachusetts Institute of Technology in Cambridge, U.S.A. Our results show that survey-elicited commuting diaries are quite reliable when examining overall commuting trends, whereas passive mobility data are more suitable for investigating individual-level commuting behavior. Furthermore, we identify the association between discrepancies in commuting behavior and certain individual characteristics, for example, employee type and age.

几十年来，交通研究人员一直使用调查数据来了解影响旅行相关选择的因素。如今，旅行调查为交通建模、规划和政策制定的旅行行为分析奠定了基础。城市传感信息技术的发展使调查引发的和被动移动数据收集方面有了实质性的改进。主动收集和被动收集的数据非常不同，能够比较和整合它们可以让利益相关者更好地了解人类的流动性。调查自我报告的旅行行为与城市和移动系统揭示的实际旅行行为之间的比较为我们提供了发现潜在差异的机会。以前的工作主要在人口水平上检查了这些差异。对这些差异进行个人层面的调查可以带来许多好处，从增加我们对调查和被动数据准确性和收集的理解，到设计个性化的交通服务。在这项研究中，通过利用可用的移动数据，即基于调查的通勤日记和被动移动记录，在个人和汇总层面分析了自我报告和观察到的旅行行为之间的差异。我们为通勤活动提出了一组我们有可用和可比数据的差异指标，并将该框架应用于美国剑桥麻省理工学院的实证分析 我们的结果表明，调查得出的通勤日记在检查整体通勤趋势时非常可靠，而被动移动数据更适合调查个人层面的通勤行为。此外，我们确定了通勤行为差异与某些个人特征（例如员工类型和年龄）之间的关联。