论文标题
通过动态线性模型的拥挤高速公路的旅行时间预测
Travel time prediction for congested freeways with a dynamic linear model
论文作者
论文摘要
准确的旅行时间预测是支持智能运输系统（ITS）的重要功能。但是，交通状态的非线性使这一预测成为一项艰巨的任务。在这里，我们建议使用动态线性模型（DLM）来近似非线性交通状态。与静态线性回归模型不同，DLMS假定其参数在跨时间发生变化。我们设计了一个在每个时间单元中定义的模型参数的DLM，以描述时间序列流量数据的时空特征。基于我们的DLM及其模型参数，使用历史数据进行了分析训练，我们建议在最小均方根误差（MMSE）意义中具有最佳的线性预测指标。我们将高速公路（I210-E和I5-S）在高度拥挤的交通条件下的旅行时间的预测准确性与其他方法的预测准确性：瞬时旅行时间，k-neartiment邻居，支持向量回归和人工神经网络。我们在准确性方面表现出显着提高，尤其是对于短期预测。
Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to approximate the non-linear traffic states. Unlike a static linear regression model, the DLMs assume that their parameters are changing across time. We design a DLM with model parameters defined at each time unit to describe the spatio-temporal characteristics of time-series traffic data. Based on our DLM and its model parameters analytically trained using historical data, we suggest an optimal linear predictor in the minimum mean square error (MMSE) sense. We compare our prediction accuracy of travel time for freeways in California (I210-E and I5-S) under highly congested traffic conditions with those of other methods: the instantaneous travel time, k-nearest neighbor, support vector regression, and artificial neural network. We show significant improvements in the accuracy, especially for short-term prediction.
