A time-varying driving style oriented model predictive control for smoothing mixed traffic flow

Haoli Lou, Hao Lyu and Rongjun Cheng

Physica A: Statistical Mechanics and its Applications, 2024, vol. 637, issue C

Abstract: Connected and automated vehicles (CAVs) have great potential to smooth mixed traffic flow. To focus on the fact that the driving styles of HDVs are time-varying, a new control framework based on time-varying model predictive control (MPC) is proposed for mixed traffic flow in longitudinal control. Firstly, a genetic algorithm can be employed to calibrate the parameters of the car-following model based on offline trajectory data, representing the long-term driving style of HDVs. Secondly, the short-term driving style is recognized through modifying the long-term driving style based on real-time trajectory data within a sliding window. Then, this paper seeks to design a time-varying MPC control framework to realize a smoother mixed traffic flow. Finally, compared to the baseline model, the method we proposed achieves the high-performance both in fuel consumption and driving comfort under various scenarios, and maintains fast online calculations. Besides, it also displays the significant potential of saving fuel consumption in a large-scale traffic system, reducing by over 11.28% with 17∼33% penetration rates of CAVs.

Keywords: Model predictive control; Time-varying driving style; Mixed traffic flow; Longitudinal control (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124001146

DOI: 10.1016/j.physa.2024.129606

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