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

TL;DR

This paper introduces a novel microscopic traffic model inspired by mechanical systems, extending previous two-vehicle models to multi-vehicle scenarios, with stability analysis and real-time parameter estimation validated on real driving data.

Contribution

The paper presents a multi-vehicle mechanical system inspired traffic model with a new string stability criterion and an efficient online parameter identification algorithm validated on real datasets.

Findings

Model accurately captures vehicle interaction dynamics.

Stability analysis provides insights into system parameters and delays.

Real-time parameter estimation improves trajectory prediction.

Abstract

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.