A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips
A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips
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DOI：
10.3390/su11154214
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发表时间：
2019-08
期刊：
影响因子：
3.9
通讯作者：
Yong Gao;Jiajun Liu;Yan Xu;Lan Mu;Yu Liu
中科院分区：
文献类型：
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作者：
Yong Gao;Jiajun Liu;Yan Xu;Lan Mu;Yu Liu
出租车服务为市民提供了一种城市交通选择。大量的出租车轨迹包含了丰富的信息，用于了解人类的出行活动，这对可持续的城市移动和交通至关重要。城市出租车出行的O-D对可以揭示出城市人口流动的时空格局，为城市的形式、功能和感知区域的解读和改革提供基础信息。矩阵是表示出租车轨迹和O-D行程的最有效的模型之一。在矩阵表示中，非负矩阵分解（NMF）对复杂的潜在关系给出了有意义的解释。然而，出租车流的空间和时间的自相关性违反了观测的独立性假设，这在经典的NMF模型中没有得到补偿。为了发现人类在城市内的移动模式，时空约束NMF（STC-NMF）模型，显式地解决了空间和时间的依赖性，本文提出了一种新的。它分解出租车流量矩阵在空间和时间方面，从而揭示了内在的时空模式。利用北京市出租车3个月的运行轨迹数据，利用STC-NMF模型研究了出租车出行模式及其空间相互作用模式。结果发现，在工作日和周末的四个出发模式，三个到达模式，和八个空间相互作用模式。此外，研究发现，在一定的时间窗口内，强烈的运动显着相关的区域功能和空间的相互作用流表现出明显的距离衰减趋势。所提出的模型的结果是更符合固有的时空特性的人在城市内的运动。本研究获得的知识将有助于出租车服务和交通管理，促进城市可持续发展。
Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.