Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

Wang, Jiawei; Lian, Yingzhao; Jiang, Yuning; Xu, Qing; Li, Keqiang; Jones, Colin N.

doi:10.1016/j.trc.2023.104274

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…Some researchers proposed a comprehensive linear time-varying MPC design for a type of AV to achieve good trajectory tracking in a practical driving scenario [45]. Some researchers proposed a model of predictive control based on ACC to dampen traffic waves and reduce traffic congestion [46,47]. A safety-enhancing eco-driving strategy for CAVs based on a hierarchical distributed framework was proposed to optimize the trajectory of CAVs on a signalized arterial in mixed traffic flow [48].…”

Section: Introductionmentioning

confidence: 99%

A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic

Li,

Roncoli,

2024

Applied Sciences

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Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway.

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