这篇论文用了一个简单的规则方法，在IEEE竞赛中拿了第二，比机器学习方法还厉害。
本文提出基于规则的访问循环（RVC）模型，用于解决IEEE Big Data Cup 2022的行程目的地预测任务。该任务给定4个训练大都市区的行程数据，要求预测目标大都市区的目的地，而目标区的目的地完全未知。RVC通过利用重复访问行为和出发地-目的地关系，无需从训练区学习即可直接预测。在离线评估和排行榜提交中，RVC显著优于监督学习方法和其他启发式方法，最终获得比赛第二名。
An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction
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.