Metro operation management relies on accurate predictions of passenger flow in the future. This study begins by integrating cross-city (including source and target city) knowledge and developing a short-term passenger flow prediction framework (METcross) for the metro. Firstly, we propose a basic framework for modeling cross-city metro passenger flow prediction from the perspectives of data fusion and transfer learning. Secondly, METcross framework is designed to use both static and dynamic covariates as inputs, including economy and weather, that help characterize station passenger flow features. This framework consists of two steps: pre-training on the source city and fine-tuning on the target city. During pre-training, data from the source city trains the feature extraction and passenger flow prediction models. Fine-tuning on the target city involves using the source city's trained model as the initial parameter and fusing the feature embeddings of both cities to obtain the passenger flow prediction results. Finally, we tested the basic prediction framework and METcross framework on the metro networks of Wuxi and Chongqing to experimentally analyze their efficacy. Results indicate that the METcross framework performs better than the basic framework and can reduce the Mean Absolute Error and Root Mean Squared Error by 22.35% and 26.18%, respectively, compared to single-city prediction models.

翻译：地铁运营管理依赖于对未来客流的准确预测。本研究首先整合跨城市（包括源城市与目标城市）知识，构建了一种地铁短期客流预测框架（METcross）。首先，我们从数据融合与迁移学习的角度，提出了跨城市地铁客流预测的基础建模框架。其次，METcross框架设计为同时使用静态与动态协变量作为输入，包括经济与天气因素，以帮助刻画站点客流特征。该框架包含两个步骤：在源城市进行预训练，在目标城市进行微调。预训练阶段利用源城市数据训练特征提取与客流预测模型。目标城市的微调则以源城市训练模型为初始参数，并融合两城市的特征嵌入，从而获得客流预测结果。最后，我们在无锡与重庆的地铁网络上测试了基础预测框架与METcross框架，以实验分析其有效性。结果表明，METcross框架表现优于基础框架，相较于单城市预测模型，其平均绝对误差与均方根误差分别降低了22.35%与26.18%。