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

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’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.

I. Introduction

Rising traffic congestion has become an increasingly frustrating societal problem, especially in large metropolitan areas across the globe. It has led to a variety of issues including great loss in time and money [1], elevated stress and frustration in drivers [2], and intensified air pollution [3]. Based on a recent report from INRIX [1], traffic congestion cost U.S. more than 300 billion dollars and drivers in big cities spent more than 100 hours in congestion in the year of 2017 alone. A number of traffic control technologies have thus been pursued to mitigate the congestion, including ramp metering [4], [5], dynamic speed limits [6], [7], vehicle platooning [8], [9], and active traffic light control [10]–[12]. It is worth noting that all those technologies require accurate estimation and prediction of real-time traffic, which creates a critical need to have a good understanding of the traffic dynamics and flow.