关键词: 城市交通, 行程时间, 多步预测, Karhunen-Loeve变换, 时间序列重构

Abstract:

Road multi interval travel time forecasting data is an important parameter for dynamic traffic guidance system. But previously developed models have some deficiency, such as bad adaptability, large amount of calculation needing and many history data requirement. Applied the decomposition, reconstruction of spectral analysis and Karhunen-Loeve (K-L) transform method to decompose and reconstruct the history and current detection travel time series. Euclidean distance is used to measure the closeness between current and historical travel time series, by the means of optimization the eigenvector coefficient to make those more closely history series has the more weight in the reconstruction and then gain the goal of road travel time multi step forecasting. The case study suggest that, the proposed method can fulfill multi step road travel time prediction and has a good prediction accuracy, some better than the previous method, furthermore, a fewer history data and calculation resources needing.

Key words: urban traffic, travel time, multi-step prediction, Karhunen-Loeve transform, time series reconstruction