  In this paper, we develop a mechanical system inspired microscopic traffic
model to characterize the longitudinal interaction dynamics among a chain of
vehicles. In particular, we extend our prior work on mass-spring-damper-clutch
based car-following model between two vehicles to multi-vehicle scenario. This
model can naturally capture the driver's tendency to maintain the same speed as
the vehicle ahead while keeping a (speed-dependent) desired spacing. It is also
capable of characterizing the impact of the following vehicle on the preceding
vehicle, which is generally neglected in existing models. A new string
stability criterion is defined for the considered multi-vehicle dynamics, and
stability analysis is performed on the system parameters and time delays. An
efficient online parameter identification algorithm, sequential recursive least
squares with inverse QR decomposition (SRLS-IQR), is developed to estimate the
driving-related model parameters. These real-time estimated parameters can be
employed in advanced longitudinal control systems to enable accurate prediction
of vehicle trajectories for improved safety and fuel efficiency. The proposed
model and the parameter identification algorithm are validated on NGSIM, a
naturalistic driving dataset, as well as our own connected vehicle driving
data. Promising performance is demonstrated.
