融合Tucker分解和深度学习的出租车需求预测——一种城市出租车需求预测的轻量化解决方案
Incorporating Tucker Decomposition and Deep Learning for Taxi Demand Forecasting—A Lightweight Solution for Urban Taxi Demand Forecasting
DOI: 10.12677/sea.2024.135068, PDF,    国家自然科学基金支持
作者: 楚本嘉, 颜鸿宇, 李建波*：青岛大学计算机科学技术学院，山东 青岛；青岛大学泛在网络与城市计算研究所，山东 青岛
关键词: 出租车需求预测；时空预测模型；Tucker分解；Taxi Demand Forecasting； Spatial-Temporal Prediction Model； Tucker Decomposition
摘要: 城市出租车需求预测在降低出租车空车行驶率、缓解道路交通拥堵方面发挥着重要作用。然而，由于城市路网结构的复杂性，出租车流量的准确预测一直是一项挑战。为了更好地捕捉出租车数据的空间特征，准确预测未来的需求变化，我们提出了一种新颖的时空预测模型。该模型融合了Tucker分解和深度学习的优势，不仅能够捕获出租车需求数据之间的时空相关性，还考虑到了外部因素的潜在影响。最终，通过对五个真实世界的数据集进行出租车需求预测实验，我们验证了本文提出的模型在预测性能方面的有效性。
Abstract: Urban taxi demand forecasting plays an important role in reducing empty cab trips and easing road traffic congestion. However, accurate prediction of cab flows has been a challenge due to the complexity of urban road network structures. To better capture the spatial characteristics of cab data and accurately predict future demand changes, we propose a novel spatial-temporal prediction model. The model incorporates the strengths of Tucker decomposition and deep learning to not only capture the spatial-temporal correlation between cab demand data, but also take into account the potential impact of external factors. Ultimately, by conducting cab demand prediction experiments on five real-world datasets, we validate the effectiveness of the model proposed in this paper in terms of prediction performance.
文章引用：楚本嘉, 颜鸿宇, 李建波. 融合Tucker分解和深度学习的出租车需求预测——一种城市出租车需求预测的轻量化解决方案[J]. 软件工程与应用, 2024, 13(5): 660-669. https://doi.org/10.12677/sea.2024.135068

参考文献

[1] Yang, H., Wong, K.I. and Wong, S.C. (2001) Modeling Urban Taxi Services in Road Networks: Progress, Problem and Prospect. Journal of Advanced Transportation, 35, 237-258. [Google Scholar] [CrossRef] 
[2] Markou, I., Kaiser, K. and Pereira, F.C. (2019) Predicting Taxi Demand Hotspots Using Automated Internet Search Queries. Transportation Research Part C: Emerging Technologies, 102, 73-86. [Google Scholar] [CrossRef] 
[3] Kolda, T.G. and Bader, B.W. (2009) Tensor Decompositions and Applications. SIAM Review, 51, 455-500. [Google Scholar] [CrossRef] 
[4] Xu, X., Zhang, T., Xu, C., Cui, Z. and Yang, J. (2023) Spatial-Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction. IEEE Transactions on Intelligent Transportation Systems, 24, 92-103. [Google Scholar] [CrossRef] 
[5] Xu, Z., Lv, Z., Chu, B. and Li, J. (2023) Fast Autoregressive Tensor Decomposition for Online Real-Time Traffic Flow Prediction. Knowledge-Based Systems, 282, Article 111125. [Google Scholar] [CrossRef] 
[6] Shao, P., Zhang, D., Yang, G., Tao, J., Che, F. and Liu, T. (2022) Tucker Decomposition-Based Temporal Knowledge Graph Completion. Knowledge-Based Systems, 238, Article 107841. [Google Scholar] [CrossRef] 
[7] Frandsen, A. and Ge, R. (2020) Optimization Landscape of Tucker Decomposition. Mathematical Programming, 193, 687-712. [Google Scholar] [CrossRef] 
[8] Faghih, S., Shah, A., Wang, Z., Safikhani, A. and Kamga, C. (2020) Taxi and Mobility: Modeling Taxi Demand Using ARMA and Linear Regression. Procedia Computer Science, 177, 186-195. [Google Scholar] [CrossRef] 
[9] Yan, Z. and Lv, S. (2021) Short-Term Forecast Model of Taxi Demand Based on Time and Space Heterogeneity. Journal of Intelligent & Fuzzy Systems, 41, 4175-4186. [Google Scholar] [CrossRef] 
[10] Jiao, P., Li, R., Sun, T., Hou, Z. and Ibrahim, A. (2016) Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction. Mathematical Problems in Engineering, 2016, 1-10. [Google Scholar] [CrossRef] 
[11] Liu, Y., Qiao, J., Han, T., Li, L. and Xu, T. (2020) A 3D Image Reconstruction Model for Long Tunnel Geological Estimation. Journal of Advanced Transportation, 2020, 1-10. [Google Scholar] [CrossRef] 
[12] Liu, Z., Chen, H., Sun, X. and Chen, H. (2020) Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method. Applied Sciences, 10, Article No. 6681. [Google Scholar] [CrossRef] 
[13] Luo, H., Cai, J., Zhang, K., Xie, R. and Zheng, L. (2021) A Multi-Task Deep Learning Model for Short-Term Taxi Demand Forecasting Considering Spatiotemporal Dependences. Journal of Traffic and Transportation Engineering, 8, 83-94. [Google Scholar] [CrossRef] 
[14] Chu, K., Lam, A.Y.S. and Li, V.O.K. (2020) Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions. IEEE Transactions on Intelligent Transportation Systems, 21, 3219-3232. [Google Scholar] [CrossRef] 
[15] Xu, Z., Lv, Z., Li, J., Sun, H. and Sheng, Z. (2023) A Novel Perspective on Travel Demand Prediction Considering Natural Environmental and Socioeconomic Factors. IEEE Intelligent Transportation Systems Magazine, 15, 136-159. [Google Scholar] [CrossRef] 
[16] Lai, Y., Zhang, K., Lin, J., Yang, F. and Fan, Y. (2019) Taxi Demand Prediction with LSTM-Based Combination Model. 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, Xiamen, 16-18 December 2019, 944-950. [Google Scholar] [CrossRef] 
[17] Liao, W., Zeng, B., Liu, J., Wei, P. and Cheng, X. (2022) Taxi Demand Forecasting Based on the Temporal Multimodal Information Fusion Graph Neural Network. Applied Intelligence, 52, 12077-12090. [Google Scholar] [CrossRef] 
[18] Luo, A., Shangguan, B., Yang, C., Gao, F., Fang, Z. and Yu, D. (2022) Spatial-Temporal Diffusion Convolutional Network: A Novel Framework for Taxi Demand Forecasting. ISPRS International Journal of Geo-Information, 11, Article No. 193. [Google Scholar] [CrossRef] 
[19] Li, Y., Li, J., Lv, Z., Li, H., Wang, Y. and Xu, Z. (2023) GASTO: A Fast Adaptive Graph Learning Framework for Edge Computing Empowered Task Offloading. IEEE Transactions on Network and Service Management, 20, 932-944. [Google Scholar] [CrossRef] 
[20] Lv, Z., Li, J., Xu, Z., Wang, Y. and Li, H. (2021) Parallel Computing of Spatio-Temporal Model Based on Deep Reinforcement Learning. In: Liu, Z., Wu, F. and Das, S.K., Eds., Wireless Algorithms, Systems, and Applications, Springer, 391-403. [Google Scholar] [CrossRef] 

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