Stochastic parameter-optimized car-following model considering heterogeneous traffic flow
In order to examine the impact of traffic flow heterogeneity on vehicle following behavior, we propose an improved optimized speed function based on the stochastic parametric linear regression method. The speed-density data for traffic flow are categorized using quantile regression. Random parameter...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press (China Science Publishing & Media Ltd.)
2024-07-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
| Subjects: | |
| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2656 |
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| Summary: | In order to examine the impact of traffic flow heterogeneity on vehicle following behavior, we propose an improved optimized speed function based on the stochastic parametric linear regression method. The speed-density data for traffic flow are categorized using quantile regression. Random parameter linear regression is then applied to each data category, resulting in improved optimal velocity function and hypothesis testing for each category. The stochastic optimal velocity car-following model is developed by integrating the enhanced optimal velocity function with full velocity difference car-following model. The stability of the car-following model is analyzed by applying Fourier transform theory. Numerical experiments on the car-following model are conducted through a simulation platform for circular lanes. The results indicate that categorization reduces the error of the random parameter model by 28% compared to the model without categorization. Additionally, the speed of the random parameter car-following fleet increases with the addition of 0.5 quantile vehicles. The random parameter car-following model fleet is better suited to reflect the impact of traffic flow heterogeneity on the fleet than the fixed parameter car-following model fleet. The model can enhance the simulation aspect and accurately depict the intricate functioning of traffic flow. |
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| ISSN: | 1000-2618 |