Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control
The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/6/481 |
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| Summary: | The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. Due TV-MPC can be applied to any traffic congestion scenario and the dynamic modeling that considers driving style, can be easily transferred to other control algorithms. Thus, TV-MPC enable to represent typical control algorithms in mixed traffic flow. This study investigates the performance of TV-MPC under diverse disturbance characteristics and mixed platoons. Firstly, quantifying mixed traffic flow with different CAV penetration rates and platooning intensities by a Markov chain model. Secondly, by constructing evaluation indicators for micro-level operation of mixed traffic flow, this paper analyzed the impact of TV-MPC on the operation of mixed traffic flow through simulation. The results demonstrate that (1) CAV achieve optimal control at specific positions within mixed traffic flow; (2) higher CAV penetration enhances TV-MPC performance; (3) dispersed CAV distributions improve control effectiveness; and (4) TV-MPC excels in scenarios with significant disturbances. |
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| ISSN: | 2079-8954 |