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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MDPI AG
2025-06-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/6/481 |
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| author | Rongjun Cheng Haoli Lou Qi Wei |
| author_facet | Rongjun Cheng Haoli Lou Qi Wei |
| author_sort | Rongjun Cheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1eddb56e3a643a6931e0978a403d48e |
| institution | OA Journals |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-d1eddb56e3a643a6931e0978a403d48e2025-08-20T02:21:50ZengMDPI AGSystems2079-89542025-06-0113648110.3390/systems13060481Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive ControlRongjun Cheng0Haoli Lou1Qi Wei2Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaCollege of International Economics and Trade, Ningbo University of Finance and Economics, Ningbo 315175, ChinaThe 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.https://www.mdpi.com/2079-8954/13/6/481model predictive controltime-varying driving stylemixed traffic flowMarkov chain model |
| spellingShingle | Rongjun Cheng Haoli Lou Qi Wei Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control Systems model predictive control time-varying driving style mixed traffic flow Markov chain model |
| title | Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control |
| title_full | Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control |
| title_fullStr | Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control |
| title_full_unstemmed | Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control |
| title_short | Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control |
| title_sort | analysis of the impact for mixed traffic flow based on the time varying model predictive control |
| topic | model predictive control time-varying driving style mixed traffic flow Markov chain model |
| url | https://www.mdpi.com/2079-8954/13/6/481 |
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