Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets
To fully utilize the advantages of Connected and Automated Vehicles (CAV) in mixed traffic flow, this study focuses on investigating the car-following characteristics of mixed traffic flow. First, the potential car-following patterns in mixed traffic are analyzed, and an improvement to the Intellige...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10972016/ |
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| author | Huanfeng Liu Hanfei Wang Ziyan Wu Anning Song Zishuo Zhang |
| author_facet | Huanfeng Liu Hanfei Wang Ziyan Wu Anning Song Zishuo Zhang |
| author_sort | Huanfeng Liu |
| collection | DOAJ |
| description | To fully utilize the advantages of Connected and Automated Vehicles (CAV) in mixed traffic flow, this study focuses on investigating the car-following characteristics of mixed traffic flow. First, the potential car-following patterns in mixed traffic are analyzed, and an improvement to the Intelligent Driver Model (IDM) is made by incorporating dynamic response times. Next, based on the NGSIM and Gunter datasets, Genetic Algorithm (GA) are employed to calibrate and evaluate IDM parameters. Subsequently, an analysis of mixed traffic flow characteristics is conducted, including a sensitivity analysis of key parameters. Finally, the stability and safety of mixed traffic flow are analyzed. The results demonstrate that the improved numerical simulations closely match real-world data. CAV can enhance the stability of mixed traffic flow under certain conditions. The improved model provides a theoretical basis for analyzing future traffic flow characteristics. |
| format | Article |
| id | doaj-art-e8adddb0b928487a825c321bfa6a93a0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e8adddb0b928487a825c321bfa6a93a02025-08-20T02:14:49ZengIEEEIEEE Access2169-35362025-01-0113746677467710.1109/ACCESS.2025.356311710972016Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical DatasetsHuanfeng Liu0https://orcid.org/0009-0006-7191-0273Hanfei Wang1https://orcid.org/0009-0009-3771-0165Ziyan Wu2https://orcid.org/0009-0001-7351-0023Anning Song3https://orcid.org/0009-0008-0927-3984Zishuo Zhang4https://orcid.org/0009-0003-3294-3951School of Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaTo fully utilize the advantages of Connected and Automated Vehicles (CAV) in mixed traffic flow, this study focuses on investigating the car-following characteristics of mixed traffic flow. First, the potential car-following patterns in mixed traffic are analyzed, and an improvement to the Intelligent Driver Model (IDM) is made by incorporating dynamic response times. Next, based on the NGSIM and Gunter datasets, Genetic Algorithm (GA) are employed to calibrate and evaluate IDM parameters. Subsequently, an analysis of mixed traffic flow characteristics is conducted, including a sensitivity analysis of key parameters. Finally, the stability and safety of mixed traffic flow are analyzed. The results demonstrate that the improved numerical simulations closely match real-world data. CAV can enhance the stability of mixed traffic flow under certain conditions. The improved model provides a theoretical basis for analyzing future traffic flow characteristics.https://ieeexplore.ieee.org/document/10972016/Mixed traffic flowcar-following modelgenetic algorithmcharacteristic analysis |
| spellingShingle | Huanfeng Liu Hanfei Wang Ziyan Wu Anning Song Zishuo Zhang Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets IEEE Access Mixed traffic flow car-following model genetic algorithm characteristic analysis |
| title | Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets |
| title_full | Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets |
| title_fullStr | Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets |
| title_full_unstemmed | Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets |
| title_short | Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets |
| title_sort | improvement and characteristic analysis of car following models in mixed traffic flow based on empirical datasets |
| topic | Mixed traffic flow car-following model genetic algorithm characteristic analysis |
| url | https://ieeexplore.ieee.org/document/10972016/ |
| work_keys_str_mv | AT huanfengliu improvementandcharacteristicanalysisofcarfollowingmodelsinmixedtrafficflowbasedonempiricaldatasets AT hanfeiwang improvementandcharacteristicanalysisofcarfollowingmodelsinmixedtrafficflowbasedonempiricaldatasets AT ziyanwu improvementandcharacteristicanalysisofcarfollowingmodelsinmixedtrafficflowbasedonempiricaldatasets AT anningsong improvementandcharacteristicanalysisofcarfollowingmodelsinmixedtrafficflowbasedonempiricaldatasets AT zishuozhang improvementandcharacteristicanalysisofcarfollowingmodelsinmixedtrafficflowbasedonempiricaldatasets |