A novel tucker decomposition driven taxi travel demand forecasting algorithm

Benjia Chu, Hongyu Yan

Article ID: 2957
Vol 2, Issue 2, 2024
DOI: https://doi.org/10.54517/mss2957
Received: 23 September 2024; Accepted: 22 October 2024; Available online: 10 November 2024; Issue release: 15 November 2024


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Abstract

This research aims to enhance taxi travel demand forecasting for sustainable urban traffic management and planning. We extend the Seasonal Autoregressive Integrated Moving Average model into a high-dimensional tensor form by treating the urban transport network as a Euclidean space and introducing Tucker decomposition. This novel approach, both theoretically significant and practically applicable, represents time series data as tensors to better capture multimodal structures and correlations, improving predictive accuracy. Tucker decomposition reduces computational complexity and memory requirements, making it ideal for large-scale urban traffic network prediction. Experiments on six real-world datasets show that the model’s MAE and RMSE are reduced by about 39.43% and 27.01% on average, respectively, compared to the baseline model. Notably, the model is computationally very efficient and takes only a relatively short time to train., suitable for real-time traffic management, congestion mitigation, and resource optimization. In summary, this work innovates time series analysis, providing an efficient and precise tool for urban traffic management and planning, contributing to sustainable urban transportation advancement.


Keywords

taxi travel demand forecasting; tucker decomposition; seasonal autoregressive integrated moving average


References

Cheng Z, Jian S, Rashidi T H, et al. Integrating household travel survey and social media data to improve the quality of od matrix: A comparative case study. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(6): 2628-2636.doi: 10.1109/TITS.2019.2958673 Xu Z, Li J, Lv Z, et al. A classification method for urban functional regions based on the transfer rate of empty cars. IET Intelligent Transport Systems, 2022, 16(2): 133-147. doi: 10.1049/itr2.12134 Lv Z, Li, J, Dong C, Li H, et al. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index. Data & Knowledge Engineering, 2021,135, 101912. doi: 10.1016/j.datak.2021.101912 Li J, Lv Z, Ma Z, Wang X, et al. Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree. Information Fusion, 2024,104, 102178. doi: 10.1016/j.inffus.2023.102178 Lv Z, Ma Z, Xia F, et al. A transportation Revitalization index prediction model based on Spatial-Temporal attention mechanism. Advanced Engineering Informatics,2024, 61, 102519. doi: 10.1016/j.aei.2024.102519 Li H, Lv Z, Li J, et al. Traffic flow forecasting in the covid-19: A deep spatial-temporal model based on discrete wavelet transformation. ACM Transactions on Knowledge Discovery from Data, 2023, 17(5): 1-28.doi: 10.1145/3564753 Xu Z, Lv Z, Chu B, et al. Progress and prospects of future urban health status prediction. Engineering Applications of Artificial Intelligence, 2024,129, 107573. doi: 10.1016/j.engappai.2023.107573 Xu Z, Lv Z, Li J, et al. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resources Management, 2022, 36(11): 4293-4312. doi: 10.1007/s11269-022-03255-5 Lv Z, Wang X, Cheng Z, et al. A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index. Data & Knowledge Engineering, 2023, 146: 102193. doi: 10.1016/j.datak.2023.102193 Sheng Z, Lv Z, Li J, et al. Taxi travel time prediction based on fusion of traffic condition features. Computers and Electrical Engineering, 2023, 105: 108530. doi: 10.1016/j.compeleceng.2022.108530 Li Y, Li J, Lv Z, et al. GASTO: A fast adaptive graph learning framework for edge computing empowered task offloading. IEEE Transactions on Network and Service Management, 2023, 20(2): 932-944. doi: 10.1109/TNSM.2023.3250395 Lv Z, Li J, Xu Z, et al. Parallel computing of spatio-temporal model based on deep reinforcement learning,International Conference on Wireless Algorithms, Systems, and Applications. Cham: Springer International Publishing, 2021: 391-403.doi: 10.1007/978-3-030-85928-2_31 Xu Z, Lv Z, Chu B, et al. Fast autoregressive tensor decomposition for online real-time traffic flow prediction. Knowledge-Based Systems, 2023, 282: 111125. doi: 10.1016/j.knosys.2023.111125 Lv Z, Cheng Z, Li J, et al. TreeCN: time series prediction with the tree convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 2023. doi: 10.1109/TITS.2023.3325817 Ye R, Lv Z, Xu Z, et al. MT-CNN: A Lightweight Spatial-Temporal Convolutional Neural Network for Deep Learning of Complex Trajectory Distributions based on Area Partitioning,2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024: 1-8.doi: 10.1109/IJCNN60899.2024.10651261 Xu Z, Lv Z, Chu B, et al. A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns. Chaos, Solitons & Fractals, 2024, 182: 114852. doi: 10.1016/j.chaos.2024.114852 Cyril A, Mulangi R H, George V. Modelling and forecasting bus passenger demand using time series method,2018 7th international conference on reliability, infocom technologies and optimization (trends and future directions)(ICRITO). IEEE, 2018: 460-466.doi: 10.1109/ICRITO.2018.8748443 Jiao P, Li R, Sun T, et al. Three revised Kalman filtering models for short‐term rail transit passenger flow prediction. Mathematical Problems in Engineering, 2016, 2016(1): 9717582.doi: 10.1155/2016/9717582 Milenković M, Švadlenka L, Melichar V, et al. SARIMA modelling approach for railway passenger flow forecasting. Transport, 2018, 33(5): 1113-1120.doi: 10.3846/16484142.2016.1139623 Sun Y, Leng B, Guan W. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 2015, 166: 109-121.doi: 10.1016/j.neucom.2015.03.085 Wang X, An K, Tang L, et al. Short term prediction of freeway exiting volume based on SVM and KNN. International Journal of Transportation Science and Technology, 2015, 4(3): 337-352. doi: 10.1260/2046-0430.4.3.337 Hou Y, Edara P, Chang Y. Road network state estimation using random forest ensemble learning, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017: 1-6.doi: 10.1109/ITSC.2017.8317743 Joubert J W, De Waal A. Activity-based travel demand generation using Bayesian networks. Transportation Research Part C: Emerging Technologies, 2020, 120: 102804.doi: 10.1016/j.trc.2020.102804 Guo G, Zhang T. A residual spatio-temporal architecture for travel demand forecasting. Transportation Research Part C: Emerging Technologies, 2020, 115: 102639.doi: 10.1016/j.trc.2020.102639 Li X, Xu Y, Zhang X, et al. Improving short-term bike sharing demand forecast through an irregular convolutional neural network. Transportation research part C: emerging technologies, 2023, 147: 103984. doi: 10.1016/j.trc.2022.103984 Zhao T, Huang Z, Tu W, et al. Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction. Computers, Environment and Urban Systems, 2022, 94: 101776.doi: 10.1016/j.compenvurbsys.2022.101776 Xu Z, Lv Z, Li J, et al. A Novel Perspective on Travel Demand Prediction Considering Natural Environmental and Socioeconomic Factors. IEEE Intelligent Transportation Systems Magazine (April 2022), 2-25. 2022. doi: 10.1109/MITS.2022.3162901 Tang J, Gao F, Liu F, et al. Understanding spatio-temporal characteristics of urban travel demand based on the combination of GWR and GLM. Sustainability, 2019, 11(19): 5525.doi: 10.3390/su11195525 Wang D, Yang Y, Ning S. DeepSTCL: A deep spatio-temporal ConvLSTM for travel demand prediction,2018 international joint conference on neural networks (IJCNN). IEEE, 2018: 1-8.doi: 10.1109/IJCNN.2018.8489530 Liang J, Tang J, Gao F, et al. On region-level travel demand forecasting using multi-task adaptive graph attention network. Information Sciences, 2023, 622: 161-177. doi: 10.1016/j.ins.2022.11.138 Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction, Proceedings of the AAAI conference on artificial intelligence. 2017, 31(1). doi: 10.1609/aaai.v31i1.10735 Yao H, Tang X, Wei H, et al. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction, Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 5668-5675.doi: 10.1609/aaai.v33i01.33015668 Liu L, Qiu Z, Li G, et al. Contextualized spatial–temporal network for taxi origin-destination demand prediction. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3875-3887.doi: 10.1109/TITS.2019.2915525

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