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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data
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Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data

机译：使用智能卡数据和兴趣点数据的公交旅行者区域移动模式的分析与预测

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摘要

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.
机译：地区水平的移动模式可以为人们如何聚集或离开该地区提供更多的宏观和直观的知识。然而，尚未有效地解决了区域移动模式的分析和预测。鉴于此，使用智能卡数据（SCD）和兴趣点（POI）数据，提出了一种多步方法，其集成内部受限制的模糊C-MEARELING，非负张量分解和人工神经网络（在中这篇报告。它克服了地区分工，模式提取和预测的困难。北京市的公交车SCD和POI数据用于证明该方法的有用性。北京市公交车旅行者的区域移动模式从涉及1110个地区的三阶张量提取，34个时隙，本周7天。分析结果表明，该方法在基于区域性质的基础上具有良好的性能。此外，通过考虑两个区域登机和上升模式，也可以实现区域聚集模式的预测。这些研究成果不仅可以对地区水平的人类流动模式提供深刻的洞察力，而且还支持基于证据和前瞻性的城市规划和智能运输管理。

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