Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
复制标题
通过多机制建模和集成学习进行短期交通流预测
DOI：
10.3390/app10010356
复制
发表时间：
2020-01
期刊：
影响因子：
--
通讯作者：
Jishun Ou
中科院分区：
文献类型：
--
作者：
Zhenbo Lu;Jingxin Xia;Man Wang;Qinghui Nie;Jishun Ou
短时交通流预测对于主动交通管理和控制至关重要。与任务相关的一个关键问题是如何正确地定义和捕获交通流的时间模式。一个可行的解决方案是设计一个多机制战略。提出了一种基于多区域建模和集成学习的短时交通流预测方法。首先，为了正确地捕捉不同模式的交通流动力学，状态识别模型的基础上概率建模。每个识别的政权代表一个特定的交通阶段，并被用作预测建模的代表性功能。第二，建立在集成学习策略的预测模型，它集成了多元回归树的预测。在美国加州的I-80高速公路的4个路段上采集了5 min的交通流数据，并对所提出的方法进行了评价。实验结果表明，识别出的区域能够很好地解释不同的交通相位，并在预测中发挥重要作用。此外，开发的预测模型优于四个典型的模型在均方根误差（RMSE）和平均绝对百分比误差（MAPE）的三个交通流措施。
Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.
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