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%).

随着位置获取技术的重大进步，支持GPS的设备的部署以及越来越多的移动用户，从不同的地理区域生成了大量的位置跟踪。它提供了前所未有的机会来分析和得出有关城市动态的宝贵见解，尤其是随时间变化的出行方式和特定地区的出行需求。这项工作提出了一种称为MARIO的端到端移动性关联规则挖掘框架，有助于通过分析城市的大型出租车出行痕迹来提取城市交通动态。MARIO框架包括（i）通过对出租车行程进行时空分析来生成出行动力学网络，（ii）在市区不同功能区域中发现出行需求变化，（iii）提取出行关联规则，以及（iv）使用提取的关联规则预测旅行需求和交通动态。拟议的MARIO框架在Google Cloud Platform中实现，并使用纽约绿色和黄色出租车轨迹，罗马出租车数据集和旧金山出租车数据集的真实GPS轨迹数据集进行了广泛的实验已经进行了演示以证明该框架的有效性。在预测出行需求方面，建议的方法的性能明显优于基线方法（平均MAPE值和执行时间减少了50％）。