Immobility or soft refusal? An empirical analysis of the association between respondents’ diligence and reported immobility in household travel surveys

• Proposes diligence indicators for cross-sectional, paper-based travel surveys. • Non-diligent respondents often give soft refusals. • Open-ended and item nonresponse are key signs of low diligence. • A proposal of correction method of immobility rates by diligence indicators. In household travel surveys (HTS), some respondents may report immobility despite having actually traveled on the survey day to reduce survey burden, which is an instance of soft refusal. Since this can deteriorate the data quality of HTS, detecting possible soft refusals is important for HTS organizers and users. The respondents’ diligence can be used to detect possible soft refusals, but its examination is not sufficient. The objective of this study is to explore the association between their diligence and possible soft refusals in HTS. Data from the 2023 Kumamoto Metropolitan Area Household Travel Survey in Japan were used to examine this association. Firstly, we defined five types of less diligent respondents: item nonrespondents, nonrespondents to the open-ended questions (OEQ), proxy respondents, incentive seekers, and late submitters. Then, their immobility rates were compared with those of their more diligent counterparts. Binomial logit models were estimated to investigate the association comprehensively, and the model incorporating diligence variables was used to correct possible soft refusal bias. The results suggest that most less diligent respondents are more likely to report immobility, especially for the nonrespondents to OEQ and item nonrespondents. In contrast, incentive seekers are less likely to report immobility than non-incentive seekers, and late submitters show similar immobility rates to punctual ones. These findings suggest that handling less diligent respondents helps correct the overstated immobility rates. The results of this study contribute to the assessment and improvement of HTS data quality, which is important for transportation research and policymaking.