基于动态线性模型的拥挤路段行程时间预测
Travel time prediction for congested freeways with a dynamic linear model
摘要：
行程时间的准确预测是支持智能交通系统（ITS）的一个重要特征。然而，交通状态的非线性使得这种预测成为一项具有挑战性的任务。在这里，我们建议使用动态线性模型（DLMs）来近似的非线性交通状态。与静态线性回归模型不同，DLM假设其参数随时间变化。我们设计了一个DLM模型，在每个时间单元定义的模型参数来描述时间序列交通数据的时空特性。基于我们的DLM和它的模型参数分析训练使用历史数据，我们建议一个最佳的线性预测在最小均方误差（MMSE）的意义。我们比较我们的预测精度的高速公路在加利福尼亚州（I210-E和I5-S）高度拥挤的交通条件下的行程时间与其他方法：瞬时行程时间，k-最近邻，支持向量回归，人工神经网络。我们显示出显着的改善的准确性，特别是短期预测。
英文摘要：
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.
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