Naturalistic Driving Data for the Analysis of Car-following Models

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides 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. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi