Impact Investigation of the Car-Following Guidance System on Microscopic Car-Following Behavior by Using Field Test Data Sets

Emergent connected vehicle (CV) technology can enhance driver perception accuracy and provide auxiliary information, anticipated to improve car-following (CF) performance. However, due to a scarcity of CV field test data, the analytical impact of connected vehicles on CF requires further study. To address research needs, this study investigates CF performance within a connected vehicle environment by developing and calibrating a CV-CF model according to field test data. The proposed CV-CF model accounts for reactions due to both driving experiences and system guidance. To gauge reactions derived from driving experience, this study calibrates four naturalistic CF models using data that exclude CV car-following guidance. Meanwhile, the impact of system guidance is assessed by the difference between simulated outputs from a calibrated CF model and actual CF behaviors influenced by CF guidance. The results indicate that CF guidance provides an effective stimulus for drivers to follow the desired speed.