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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. Compared with the existing car-following (CF) models, this model offers physically interpretable insights into the underlying CF dynamics and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in the 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 systems 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 the 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 into advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable, and therefore, it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both the simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated.

A New Microscopic Traffic Model Using a Spring-Mass-Damper-Clutch System