AI 中文总结
针对2022年IEEE大数据杯出行目的地预测挑战，提出基于规则的访问循环（RVC）模型。该模型利用出发地信息和个人出行行为确定目标大都市区目的地，无需从训练区域学习，实验表明其性能优于监督学习方法和其他启发式方法，在竞赛中获第二名。
AI 中文摘要
在本文中，我们提出基于规则的访问循环（RVC）模型来应对2022年IEEE大数据杯挑战赛中的出行目的地预测挑战。给定包含训练大都市区内旅行信息、个人属性、出发区域及其特征的行程，任务是预测目标大都市区中每个行程的目的地，而这些目的地在训练阶段完全未知。我们强调了此目的地预测任务中的挑战——对目标大都市区的目的地一无所知。我们从数据集中获得见解，其中重访行为以及出发地与目的地之间的关系在个人行程中起着关键作用。因此，我们设计了一种简单但全面的方法，即基于规则的访问循环，它直接利用出发地信息和个人出行行为来确定目标大都市区的目的地，即无需从四个训练区域进行学习。离线评估和排行榜提交的实验结果一致表明，所提出的RVC能够显著优于监督学习方法和其他启发式方法。RVC方法最终使我们在竞赛排行榜上获得第二名。
英文摘要
In this paper, we propose the Rule-based Visiting Circulation (RVC) model in tackling the challenge in the IEEE Big Data Cup 2022: Trip Destination Prediction. Given trips containing travel information, personal attributes, origin zones, and their features in the training metropolitan areas, the task is to predict the destination of every trip in a targeted metropolitan area whose destinations are not given at all at the training stage. We highlight the challenges in this destination prediction task -- having no knowledge of the destinations in the targeted metropolitan area. We provide insights from the datasets, in which revisiting behaviors and the relationships between origins and destinations play a crucial role in individuals' trips. Hence, we design a simple but comprehensive method, rule-based visiting circulation, which directly utilizes the origin information and individuals' trip behaviors to determine the destinations in the targeted metropolitan area, i.e., requiring no learning from the four training areas. Experimental results on both offline evaluation and leaderboard submission consistently exhibit the proposed RVC can significantly outperform supervised learning methods and other heuristics. The RVC method eventually brings us to second place in the competition leaderboard.
Comments8 pages
Journal ref2022 IEEE International Conference on Big Data (Big Data)
DOI：10.1109/BigData55660.2022.10020650
