Abstract
Urban transfer learning transfers knowledge from the data-rich city to the data-scarce city, effectively solving the cold-start crowd flow prediction problem. In urban transfer learning, the selection of source cities mainly focuses on the experimental evaluation, which lacks methods for assessing the transferability of source cities. Besides, the complex regional matching relationships between source-target cities have not been fully addressed. To resolve these challenges, we propose a cross-city crowd flow prediction framework based on transfer learning, called AreaTransfer. AreaTransfer aims to select the appropriate source city from multi-source candidate cities and establish effective area matching relationships to improve crowd flow prediction accuracy. First, we design a source city selection algorithm based on the city’s layout characteristics to select the final source city. Then, we propose a modified deep residual neural network to allow area-level prediction. Finally, we optimize the pre-trained model by integrating the area matching results during the city selection process. Experimental results exhibit that AreaTransfer can improve the prediction accuracy by 15%–17% compared with other state-of-the-art models.
This work is supported by the National Natural Science Foundation of China (NSFC) (Grants No. U19A2061, No. 61772228), National key research and development program of China under Grant No. 2017YFC1502306.
Original data are retrieved from Didi Chuxing, https://gaia.didichuxing.com, under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. All data are fully anonymized.
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Wei, X., Guo, T., Yu, H., Li, Z., Guo, H., Li, X. (2022). AreaTransfer: A Cross-City Crowd Flow Prediction Framework Based on Transfer Learning. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_22
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