  Microscopic traffic models describe how cars interact with their neighbors in
an uninterrupted traffic flow and are frequently used for reference in advanced
vehicle control design. In this paper, we propose a novel mechanical system
inspired microscopic traffic model using a mass-spring-damper-clutch system.
This model naturally captures the ego vehicle's resistance to large relative
speed and deviation from a (driver and speed dependent) desired relative
distance when following the lead vehicle. Comparing to existing car following
(CF) models, this model offers physically interpretable insights on the
underlying CF dynamics, and is able to characterize the impact of the ego
vehicle on the lead vehicle, which is neglected in existing CF models. Thanks
to the nonlinear wave propagation analysis techniques for mechanical systems,
the proposed model therefore has great scalability so that multiple
mass-spring-damper-clutch system can be chained to study the macroscopic
traffic flow. We investigate the stability of the proposed model on the system
parameters and the time delay using spectral element method. We also develop a
parallel recursive least square with inverse QR decomposition (PRLS-IQR)
algorithm to identify the model parameters online. These real-time estimated
parameters can be used to predict the driving trajectory that can be
incorporated in advanced vehicle longitudinal control systems for improved
safety and fuel efficiency. The PRLS-IQR is computationally efficient and
numerically stable so it is suitable for online implementation. The traffic
model and the parameter identification algorithm are validated on both
simulations and naturalistic driving data from multiple drivers. Promising
performance is demonstrated.
