论文标题
小波增强回归概况（翘曲）：随着复发性拥挤的旅行时间序列的长期估计改进
Wavelet Augmented Regression Profiling (WARP): improved long-term estimation of travel time series with recurrent congestion
论文作者
论文摘要
可靠的典型旅行时间估计值使道路使用者可以转发计划旅行以最大程度地减少旅行时间，从而可能提高整体系统效率。但是，在繁忙的高速公路上，交通拥堵可能会导致旅行时间的大型短期峰值。这些尖峰可以在与前进计划相关的小时到几天的时间尺度上使用标准时间序列模型对旅行时间进行直接预测。问题在于，某些此类尖峰是由不可预测的事件引起的，应被过滤掉，而其他峰值则是由需求的经常性峰引起的，应将其纳入估计值。在这里，我们介绍了小波增强回归分析（WARP）方法，以长期估算典型的旅行时间。经线将旅行时间的历史时间序列分解为两个组成部分：背景和尖峰。然后，它将尖峰与复发和残留充血的贡献分开。这是使用小波变换，光谱滤波和局部加权回归的组合来实现的。然后，以准确且计算廉价的方式将背景和反复的拥塞贡献用于估计典型的旅行时间。我们使用从英国国家交通信息服务（NTIS）获得的12周的链接级别旅行时间数据对英国的M6和M11高速公路进行训练和测试。在样本外验证测试中，经纱可与简单分割方法和NTIS发布的估计产生的估计值进行比较。
Reliable estimates of typical travel times allow road users to forward plan journeys to minimise travel time, potentially increasing overall system efficiency. On busy highways, however, congestion events can cause large, short-term spikes in travel time. These spikes make direct forecasting of travel time using standard time series models difficult on the timescales of hours to days that are relevant to forward planning. The problem is that some such spikes are caused by unpredictable incidents and should be filtered out, whereas others are caused by recurrent peaks in demand and should be factored into estimates. Here we present the Wavelet Augmented Regression Profiling (WARP) method for long-term estimation of typical travel times. WARP linearly decomposes historical time series of travel times into two components: background and spikes. It then further separates the spikes into contributions from recurrent and residual congestion. This is achieved using a combination of wavelet transforms, spectral filtering and locally weighted regression. The background and recurrent congestion contributions are then used to estimate typical travel times with horizon of one week in an accurate and computationally inexpensive manner. We train and test WARP on the M6 and M11 motorways in the United Kingdom using 12 weeks of link level travel time data obtained from the UK's National Traffic Information Service (NTIS). In out-of-sample validation tests, WARP compares favourably to estimates produced by a simple segmentation method and to the estimates published by NTIS.
