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In this paper, we develop a mechanical system inspired microscopic traffic model to characterize the longitudinal interaction among a chain of vehicles. In particular, we propose a mass-spring-damper-clutch based car-following (CF) model that can naturally capture the car-following behavior of a rational driver. Specifically, the spring and damper can well characterize the driver&#x2019;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.

Mechanical System Inspired Microscopic Traffic Model: Modeling, Analysis, and Validation