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Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

机译:使用时空嵌入的乘客移动模式识别的数据驱动方法

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摘要

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.
机译:城市流动模式识别在揭示人类旅行机制,发现乘客旅行宗旨以及预测和管理交通需求方面具有巨大潜力。本文旨在提出数据驱动方法,以识别基于自动票价收集(AFC)数据和基于地理数据的地铁乘客移动模式。首先,捕获在地铁站的500米范围内的信息点(POI)数据以表征站的空间属性。特别是,考虑考虑服务能力,提出了一种多源地质基础数据的融合方法,将原始POI数据转换为加权POI数据。其次,基于堆叠的自动编码器(SAE)的无监督学习框架旨在将行程的时空信息嵌入到低维密度的跳闸向量中。详细地说,嵌入的时空信息包括空间特征(围绕原点站POI类别和围绕目标站)和时间特征(开始时间,星期几,和旅行时间)。第三,引入了基于密度的聚类算法以识别基于嵌入式密集的跳闸向量的乘客移动模式。最后,北京地铁网络的情况用于验证上述方法的可行性。结果表明,该方法在识别移动模式和优于现有方法方面表现良好。

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