Empirical Verification of Car-Following Parameters Using Naturalistic Driving Data on Freeway Segments

Abstract

Microscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential component of traffic simulation is the car-following model, which defines how vehicles interact with each other and controls acceleration/deceleration to maintain a desired set of speeds and distances when constrained by a leading vehicle. Car-following models are governed by a set of parameters that define the car’s following behavior and can accommodate a range of values to reproduce desired conditions. Typically, calibration of a simulation scenario is conducted to approach a set of target macroscopic traffic condition indicators, such as speed, travel time, or queue, yet it rarely considers the accuracy of individual vehicle behavior, in part due to lack of detailed field data. In this paper, Naturalistic driving study (NDS) data sets were used to extract driving behavior on freeway segments at the microscopic level and directly characterize parameters in car-following models. The data extraction process is described, and the parameter values are illustrated for the Wiedemann 99 model implemented in commerically available software. Results highlight similarities and differences of these parameter values observed in the field and those by default in the software, and simulation outcomes upon NDS guided adjustment were analyzed. The process introduced can be expanded to similar data sets and other complex traffic conditions and therefore produce more accurate simulation results not only for metrics at a macroscopic level, but also for individual vehicle trajectories that closely mimic real-world driving.