| 引用本文: | 李立,李仕琪,徐志刚,李光泽,汪贵平.集成学习框架下的车辆跟驰行为建模[J].哈尔滨工业大学学报,2024,56(3):46.DOI:10.11918/202205090 |
| LI Li,LI Shiqi,XU Zhigang,LI Guangze,WANG Guiping.Modeling of car-following behavior under an ensemble learning framework[J].Journal of Harbin Institute of Technology,2024,56(3):46.DOI:10.11918/202205090 |
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| 摘要: |
| 为了提高复杂行驶环境下车辆跟驰行为预测精度,提出了一种集成学习框架下融合理论驱动模型和数据驱动模型的车辆跟驰行为建模方法。基于stacking集成学习框架,选择理论驱动的智能驾驶模型（IDM）、考虑车辆队列和周围行驶条件因素的数据驱动的长短时记忆（LSTM）网络和门控循环单元（GRU）网络作为跟驰行为特征的一级学习算法,选择3种线性和8种非线性回归方法作为备选二级学习算法来融合一级学习器的输出特征。通过对比使用实际车辆轨迹数据计算的模型预测精度,确定了最优模型。研究结果表明:包含车辆队列和周围行驶条件变量的数据驱动跟驰模型比IDM模型的预测精度更高；多数情况下采用非线性二级学习算法的融合跟驰模型的预测精度高于IDM模型、数据驱动跟驰模型以及采用线性二级学习算法的融合跟驰模型；分别采用GBRT回归和随机森林回归作为二级学习算法的IDM-LSTM-stacking模型和IDM-GRU-stacking模型具有最高的预测精度；外界干扰下的融合跟驰模型稳定性优于单一的理论和数据驱动跟驰模型。集成学习为驾驶行为建模提供了新方法。 |
| 关键词: 交通工程 跟驰模型 集成学习 理论驱动模型 数据驱动模型 |
| DOI：10.11918/202205090 |
| 分类号:U491.252 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2019YFB1600100)；国家自然科学基金(71901040)；陕西省自然科学基础研究计划(2021JC-28) |
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| Modeling of car-following behavior under an ensemble learning framework |
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LI Li1,LI Shiqi1,XU Zhigang2,LI Guangze1,WANG Guiping1
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（1.School of Electronic and Control Engineering, Chang′an University, Xi′an 710064, China; 2.School of Information Engineering, Chang′an University, Xi′an 710064, China）
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| Abstract: |
| In order to improve prediction accuracy of vehicle′s car-following behavior in the complex environment, a car-following behavior modeling method is proposed under an ensemble learning framework to integrate theory-driven model and data-driven model. Based on the stacking ensemble learning framework, a theory-driven model, intelligent driver model (IDM), and two data-driven models, long-short term memory (LSTM) network and gate recurrent unit (GRU) network considering the factors of vehicle platoon and surrounding driving condition, are selected as the first-level learning algorithms of car-following behavior features. Three linear and eight nonlinear regression methods are taken as the candidates of second-level learning algorithms to integrate the features of the first-level learner outputs. By comparing trajectory prediction accuracy calculated from real vehicle trajectory data, the optimal car-following model is determined. Results show that the data-driven car-following models, which take the variables of vehicle platoon and surrounding driving condition into account, have the higher trajectory prediction accuracy than the IDM. In most cases, the ensemble learning car-following models with the nonlinear second-level learning algorithm predicts vehicle trajectory in the higher accuracy than the IDM, the data-driven models and the ensemble learning car-following models with the linear second-level learning algorithm. The IDM-LSTM-stacking model, which uses the GBRT regression as second-level learning algorithm, and the IDM-GRU-stacking model, which uses the random forest regression as second-level learning algorithm, give the highest trajectory prediction accuracy. The stability of integrated car-following model under external disturbance is prior to single theory-driven model and data-driven model. The ensemble learning method provides a new approach for modeling driving behaviors. |
| Key words: traffic engineering car-following model ensemble learning theory-driven model data-driven model |
