Examining the Discrepancy between Self-Reported and Actual Commuting Behavior at the Individual Level

Travel surveys lay the foundation of transportation modeling and planning, making travel survey methods an extensively studied area. Given the rapid development of information technology and urban sensing systems, recent years have seen substantial improvements in survey-elicited and passive mobility data collection approaches. These phenomena enable strict and detailed comparisons between self-reported travel behavior extracted from travel surveys and actual travel behavior revealed by urban sensing systems such as smart cards (SC) and parking systems. Most previous work presented in literature examined this discrepancy at the population level; however, an individual-level investigation of this discrepancy is crucial and has vast potential, from informing target travel demand management to designing personalized transportation services. In this research, the discrepancy between self-reported and actual travel behavior is studied at both the individual and aggregated levels, leveraging the available mobility data, namely commuting diaries and passive mobility records. We propose a group of discrepancy measurements for two types of commuting activities (i.e., transit and driving) and apply the framework to the empirical analysis at the Massachusetts Institute of Technology (MIT). The application reveals that survey-elicited commuting diaries are relatively reliable when examining overall commuting trends, yet they are relatively less accurate when used to investigate individual-level discrepancies between reported and actual commuting behavior of MIT employees. Furthermore, this paper identifies associations between commuting discrepancies and certain individual characteristics, including employee type, age, gender, stated commuting mode, and actual commuting frequency. In addition, the distributions of discrepancy measurements across different employee groups are found to vary substantially.