多尺度融合与动态自适应图的公交客流预测模型

doi:10.3778/j.issn.1673-9418.2305107

摘要/Abstract

摘要： 公交客流预测是公共交通规划和管理中的重要问题。虽然时空图卷积在地铁客流预测任务中获得了很好的预测效果，但是面对公交更复杂的线路、大规模的节点数据，现有的基于图卷积的空间建模方法将带来巨大的空间内存消耗。同时，公交客流量短时间范围内更可能受到瞬时交通状况的影响。为了解决这些挑战，提出了一种多尺度融合和动态自适应图的公交客流预测模型（MFDAG）。该模型融合客流、时刻和周信息以增加数据的特征维度，用动态自适应图的方法来学习不同站点之间的关系。进一步提出了一种多尺度融合传播的方法来表示复杂的空间依赖关系，同时设计了一种多尺度卷积传播的方法来学习不同尺度的时间依赖关系。在两个真实的客流数据集上进行了实验，并与其他交通预测方法进行了比较。实验结果表明，所提出的多尺度融合和动态自适应图的公交客流预测方法具有更高的预测准确度。

关键词: 公交客流预测, 图采样, 动态自适应图, 多尺度融合

Abstract: Bus passenger flow prediction is a crucial issue of public transportation planning and management. Though spatio-temporal graph convolution has shown promising results for subway passenger flow prediction, the existing spatial modeling methods based on graph convolution will bring huge spatial memory consumption for complex bus lines and larger-scale node data. Additionally, bus passenger flow is significantly influenced by immediate traffic conditions within a short time. To tackle these challenges, a multi-scale fusion and dynamic adaptive graph bus passenger flow prediction model (MFDAG) is presented. The proposed model effectively integrates passenger flow, time, and weekly information to enhance the feature dimension of the data. Moreover, it employs a dynamic adaptive graph method to learn the relationships between different stations. Furthermore, a multi-scale fusion propagation method is proposed to represent the complex spatial dependency relation, and a multi-scale convolution propagation method is designed to learn the multi-scale temporal dependency relation. The experiments are conducted by using two passenger flow datasets, and the results are compared with other traffic prediction methods. Experimental results demonstrate that the proposed bus passenger flow prediction method based on multi-scale fusion and dynamic adaptive graph exhibits higher prediction accuracy.

Key words: bus passenger flow prediction, graph sampling, dynamic adaptive graph, multi-scale fusion

郭翔宇, 彭莉兰, 李崇寿, 李天瑞. 多尺度融合与动态自适应图的公交客流预测模型[J]. 计算机科学与探索, 2024, 18(7): 1879-1888.

GUO Xiangyu, PNEG Lilan, LI Chongshou, LI Tianrui. Multi-scale Fusion and Dynamic Adaptive Graph Bus Passenger Flow Prediction Model[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(7): 1879-1888.

参考文献

[1] KUMAR N, RAUBAL M. Applications of deep learning in congestion detection, prediction and alleviation: a survey[J]. Transportation Research Part C: Emerging Technologies, 2021, 133: 103432.
[2] JIN G, LIANG Y, FANG Y, et al. Spatio-temporal graph neural networks for predictive learning in urban computing: a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2023. DOI: 10.1109/TKDE.2023.3333824.
[3] LI M, ZHU Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting[C]//Proceedings of the 2021 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2021: 4189-4196.
[4] PENG H, WANG H, DU B, et al. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting[J]. Information Sciences, 2020, 521: 277-290.
[5] LI F, FENG J, YAN H, et al. Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution[J]. ACM Transactions on Knowledge Discovery from Data, 2023, 17(1): 1-21.
[6] LIANG Y, KE S, ZHANG J, et al. GeoMAN: multi-level attention networks for geo-sensory time series prediction[C]//Proceedings of the 2018 International Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 3428-3434.
[7] YAO H, TANG X, WEI H, et al. Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction[C]//Proceedings of the 2019 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2019: 5668-5675.
[8] LI Y, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: data-driven traffic forecasting[C]//Proceedings of the 2018 International Conference on Learning Representations, Vancouver, Apr 30-May 3, 2018.
[9] TORMENE P, GIORGINO T, QUAGLINI S, et al. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation[J]. Artificial Intelligence in Medicine, 2009, 45(1): 11-34.
[10] YAO H, WU F, KE J, et al. Deep multi-view spatial-temporal network for taxi demand prediction[C]//Proceedings of the 2018 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2018: 18-53.
[11] WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling[C]//Proceedings of the 2019 International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 1907-1913.
[12] ZHANG Q, CHANG J, MENG G, et al. Spatio-temporal graph structure learning for traffic forecasting[C]//Proceedings of the 2020 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2020: 1177-1185.
[13] BAI L, YAO L, LI C, et al. Adaptive graph convolutional recurrent network for traffic forecasting[C]//Advances in Neural Information Processing Systems 33, Dec 6-12, 2020: 17804-17815.
[14] CHO K, VAN B, BAHDANAU D, et al. On the properties of neural machine translation: encoder-decoder approaches[EB/OL]. [2023-03-02]. https://arxiv.org/abs/1409.1259.
[15] HAN Y, WANG C, REN Y, et al. Short-term prediction of bus passenger flow based on a hybrid optimized LSTM network[J]. ISPRS International Journal of Geo-Information, 2019, 8(9): 366.
[16] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[17] ZHENG C, FAN X, WANG C, et al. GMAN: a graph multi-attention network for traffic prediction[C]//Proceedings of the 2020 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2020: 1234-1241.
[18] ZHANG Q, LI C, SU F, et al. Spatio-temporal residual graph attention network for traffic flow forecasting[J]. IEEE Internet of Things Journal, 2023, 10(13): 11518-11532.
[19] ZHAO L, SONG Y, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858.
[20] JIN G, WANG Q, ZHU C, et al. Addressing crime situation forecasting task with temporal graph convolutional neural network approach[C]//Proceedings of the 2020 International Conference on Measuring Technology and Mechatronics Automation. Piscataway: IEEE, 2020: 474-478.
[21] YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]//Proceedings of the 2018 International Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 3634-3640.
[22] VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL]. [2023-03-02]. https://arxiv.org/abs/1710.10903.
[23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30, Long Beach, Dec 4-9, 2017: 5998-6008.
[24] ZHANG J B, ZHENG Y, QI D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Proceedings of the 2017 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2017: 1655-1661.
[25] YE Y, CHEN L, XUE F. Passenger flow prediction in bus transportation system using ARIMA models with big data[C]//Proceedings of the 2019 International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery. Piscataway: IEEE, 2019: 436-443.
[26] MOLA A J, SCHOBLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.
[27] LUO D, ZHAO D, KE Q, et al. Fine-grained service-level passenger flow prediction for bus transit systems based on multitask deep learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(11): 7184-7199.
[28] LUO D, ZHAO D, KE Q, et al. Spatiotemporal hashing multigraph convolutional network for service-level passenger flow forecasting in bus transit systems[J]. IEEE Internet of Things Journal, 2021, 9(9): 6803-6815.
[29] WU Z, PAM S, LONG G, et al. Connecting the dots: multivariate time series forecasting with graph neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 753-763.
[30] PENG L, WANG X, LU H, et al. Dynamic spatio-temporal multi-scale representation for bus Ridership prediction[C]//Proceedings of the 2023 International Joint Conference on Neural Networks. Piscataway: IEEE, 2023: 1-9.
[31] LIU L, CHEN J, WU H, et al. Physical-virtual collaboration modeling for intra-and inter-station metro ridership prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(4): 3377-3391.
[32] WANG J, JIANG J, JIANG W, et al. Libcity: an open library for traffic prediction[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. New York: ACM, 2021: 145-148.
[33] LIU J, GUAN W. A summary of traffic flow forecasting methods[J]. Journal of Highway and Transportation Research and Development, 2004, 21(3): 82-85.