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首页> 外文期刊>Journal of network and computer applications >MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories
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MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

机译：Mario：谷歌云上的时空数据挖掘框架，探讨出租车轨迹的移动动力学

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摘要

With the major advances in location acquisition techniques, deployment of GPS enabled devices and increasing number of mobile users, substantial amount of location traces are generated from different geographical regions. It provides unprecedented opportunities to analyze and derive valuable insights of urban dynamics, specifically, time-dependent mobility patterns and region-specific travel demands. This work proposes an end-to-end mobility association rule mining framework called MARIO, conducive to extract urban mobility dynamics through analysing large taxi trip traces of a city. The MARIO framework consists of (i) generating mobility-dynamics network by spatio-temporal analysis of taxi-trips, (ii) finding travel demand variations in different functional regions of the urban area, (iii) extracting mobility association rules and (iv) predicting travel demands and traffic dynamics using extracted associative rules. The proposed MARIO framework is implemented in Google Cloud Platform and an extensive set of experiments using real GPS trace dataset of NYC Green and Yellow Taxi trace, Roma Taxi Dataset and San Francisco Taxi Dataset have been carried out to demonstrate the effectiveness of the framework. The performance of the proposed approach is significantly better than the baseline methods in predicting travel demands (with the reduction of average MAPE value and execution time by 50%).
机译：通过在地点采集技术的主要进步下，GPS启用的设备的部署和越来越多的移动用户数量，从不同的地理区域产生了大量的位置迹线。它提供了前所未有的机会来分析和推导城市动态的宝贵见解，具体地，依赖时间的流动模式和特定于地区的旅行需求。这项工作提出了一项名为Mario的端到端流动性协会规则挖掘框架，有助于通过分析城市的大型出租车旅行痕迹来提取城市移动动态。 Mario框架由（i）通过出租车旅行的时空分析产生移动性 - 动力学网络，（ii）寻找城市地区不同功能区的旅行需求变化，（iii）提取移动关联规则和（四）使用提取的关联规则预测旅行需求和交通动态。拟议的Mario框架是在Google云平台中实施的，并且使用了NYC绿色和黄色出租车轨迹的真实GPS跟踪数据集的广泛的实验，Roma Taxi DataSet和San Francisco出租车数据集进行了展示框架的有效性。所提出的方法的性能明显优于预测旅行需求的基线方法（将平均MAPE值和执行时间的降低减少50％）。

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