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<p>Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions. This paper proposes a data-driven control strategy for connected and autonomous vehicles (CAVs) in mixed traffic flow, implemented through variable speed limits and lane-changing guidance. First, a cellular automata model of mixed traffic flow is developed, with adjustable CAVs’ maximum speed limit and lane-changing probability, thereby linking microscopic CAV operating rules to macroscopic traffic flow dynamics. Second, a recurrent neural network (RNN) is employed to capture the temporal dynamics of the traffic system and predict the evolution of traffic flow states. The RNN is then linearized via the Koopman operator, transforming the complex nonlinear model into a linear representation for the design of a computationally efficient model predictive controller. Finally, simulation results demonstrate that the strategy increases the average traffic speed by 14.2<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> across 12 traffic scenarios. Specifically, under the challenging conditions of high traffic density with low CAV penetration, it achieves a 5.22<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> improvement and promotes a more uniform vehicle distribution. These findings highlight the potential of the proposed approach for mixed traffic flow regulation.</p>

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A data-driven modeling approach for predictive control of mixed traffic flow

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Yue Zuo,
  • Xudong Qi,
  • Huifeng Hu,
  • Tao Zou,
  • Ping Wang

摘要

Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions. This paper proposes a data-driven control strategy for connected and autonomous vehicles (CAVs) in mixed traffic flow, implemented through variable speed limits and lane-changing guidance. First, a cellular automata model of mixed traffic flow is developed, with adjustable CAVs’ maximum speed limit and lane-changing probability, thereby linking microscopic CAV operating rules to macroscopic traffic flow dynamics. Second, a recurrent neural network (RNN) is employed to capture the temporal dynamics of the traffic system and predict the evolution of traffic flow states. The RNN is then linearized via the Koopman operator, transforming the complex nonlinear model into a linear representation for the design of a computationally efficient model predictive controller. Finally, simulation results demonstrate that the strategy increases the average traffic speed by 14.2 \(\%\) % across 12 traffic scenarios. Specifically, under the challenging conditions of high traffic density with low CAV penetration, it achieves a 5.22 \(\%\) % improvement and promotes a more uniform vehicle distribution. These findings highlight the potential of the proposed approach for mixed traffic flow regulation.