In this article, survey, sensor, and administrative data are combined to correct for survey point estimate bias due to underreporting. The response to the Dutch Road Freight Transport Survey is linked to records from a road sensor network consisting of automated weighing stations installed on highways in the Netherlands. Capture–recapture (CRC) methods are used to estimate underreporting in the survey. Heterogeneity of the vehicles with respect to capture and recapture probabilities is modeled through logistic regression and log-linear models. Six different estimators are discussed and compared. Results show a downward bias in the survey estimate due to underreporting, whereas the CRC estimators yield larger estimates. This research is a new example of multisource statistics, a promising approach to improve the benefits of sensor data in the field of official statistics.