Realising the spatio-temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern-discovering model is presented to identify the traffic regularity and characteristics from spatial and temporal dimensions. To unveil this new method, there are two main parts as follows: first, by employing the constrained projected gradient of the non-negative matrix factorisation algorithm, the original traffic data matrix is decomposed into the feature matrix and the weight matrix. Necessary constraints are newly added so that the resulting matrices are ensured to make practical sense for reflecting the traffic spatio-temporal regular pattern. Then, the self-organising maps network is further used to cluster the factorisation error into several classes representing the disparate traffic pattern of each time. In addition, the experiment is conducted on real historical data to verify the performance of the algorithm. The global urban traffic flow for a week is summarised through a set of basic patterns with related weight distribution. The well-visualised result demonstrates that the authors method can achieve significant improvement in terms of computational efficiency and accuracy when compared with other widely-used methods.

中文翻译：

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使用矩阵分解挖掘时空模式：交通流案例研究

实现城市交通的时空演化模式可以为制定个人出行路线规划和改善道路建设提供建议。提出了一种新颖的模式发现模型，以从时空维度识别交通规律和特征。为了揭示这种新方法，有两个主要部分：首先，通过采用非负矩阵分解算法的约束投影梯度，将原始交通数据矩阵分解为特征矩阵和权重矩阵。新增加了必要的约束，以确保所得矩阵对于反映交通时空规则模式具有实际意义。然后，自组织地图网络还被用于将分解误差聚类为代表每次不同流量模式的几类。另外，对真实历史数据进行了实验以验证算法的性能。通过一系列具有相关权重分布的基本模式，总结了一周的全球城市交通流量。良好的可视化结果表明，与其他广泛使用的方法相比，作者的方法可以在计算效率和准确性方面取得显着改善。