The driver-specific data available from naturalistic driving studies provide a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. This study focused on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to use naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study was based on data from the database of the 100-Car Study conducted by the Virginia Tech Transportation Institute. One thousand minutes' worth of data comprising more than 2,000 car-following events recorded across eight drivers from a section of multilane highway located near Washington, D.C., was compiled. The collected event data were used to calibrate four different car-following models, and a comparative analysis of model performance was conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed versus time and headway versus time. When compared with the Gipps, intelligent driver, and Gaxis–Herman–Rothery models, the Rakha–Pasumarthy–Adjerid model was found to perform best in matching individual drivers and in matching aggregate results.

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