Origin–destination prediction via knowledge-enhanced hybrid learning

Zeren Xing; Edward Chung; Yiyang Wang; Azusa Toriumi; Takashi Oguchi; Yuehui Wu

doi:10.1111/mice.13458

journal article Open Access Jul 01, 2025

Origin–destination prediction via knowledge-enhanced hybrid learning

Zeren Xing Edward Chung

Yiyang Wang Azusa Toriumi Takashi Oguchi Yuehui Wu

Computer-Aided Civil and Infrastructure Engineering Vol. 40 No. 17 pp. 2498-2521 · Elsevier BV

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References

61

[1]

Adeli (2005)

[2]

Alam "A dynamic ensemble learning algorithm for neural networks" Neural Computing with Applications (2020) 10.1007/s00521-019-04359-7

[3]

Almeida "Constraint-based electoral districting using a new compactness measure: An application to Portugal" Computers & Operations Research (2022) 10.1016/j.cor.2022.105892

[4]

Behara "Geographical window based structural similarity index for origin-destination matrices comparison" Journal of Intelligent Transportation Systems (2022) 10.1080/15472450.2020.1795651

[5]

Chu "Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions" IEEE Transactions on Intelligent Transportation Systems (2020) 10.1109/tits.2019.2924971

[6]

Chung, E., Ohtani, O., Warita, H., Kuwahara, M., & Morita, H. (2005). Effect of rain on travel demand and traffic accidents. Proceedings of the 8th International Conference on Intelligent Transportation Systems (ITSC), Vienna, Austria (pp. 1080–1083). 10.1109/itsc.2005.1520201

[7]

Feng, A., & Tassiulas, L. (2022). Adaptive graph spatial-temporal transformer network for traffic forecasting. Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22), New York, NY (pp. 3933–3937). 10.1145/3511808.3557540

[8]

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Shengnan Guo, Ning Feng, Chao Song et al.

Proceedings of the AAAI Conference on Artificial I... 2019 10.1609/aaai.v33i01.3301922

[9]

Han, L., Ma, X., Sun, L., Du, B., Fu, Y., Lv, W., & Xiong, H. (2022). Continuous-time and multi-level graph representation learning for origin-destination demand prediction. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), New York, NY (pp. 516–524). 10.1145/3534678.3539273

[10]

Hu, J., Yang, B., Guo, C., Jensen, C.S., & Xiong, H. (2020). Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX (pp. 1417–1428). 10.1109/icde48307.2020.00126

[11]

Huang "ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario" Expert Systems with Applications (2023) 10.1016/j.eswa.2023.119835

[12]

Huang "Interpretable local flow attention for multi-step traffic flow prediction" Neural Networks (2023) 10.1016/j.neunet.2023.01.023

[13]

Jiang "Countrywide origin-destination matrix prediction and its application for COVID-19" (2021)

[14]

Karim "Fast automatic incident detection on urban and rural freeways using wavelet energy algorithm" Journal of Transportation Engineering, ASCE (2003) 10.1061/(asce)0733-947x(2003)129:1(57)

[15]

Ke "Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network" Transportation Research Part C: Emerging Technologies (2021) 10.1016/j.trc.2020.102858

[16]

Kipf, T.N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. https://doi.org/10.48550/arXiv.1609.02907

[17]

Li "Designing a novel two-stage fusion framework to predict short-term origin–destination flow" Journal of Transportation Engineering, Part A: Systems (2023)

[18]

Li "A hybrid algorithm for estimating origin-destination flows" IEEE Access (2017) 10.1109/access.2017.2774449

[19]

Li "Taxi booking mobile app order demand prediction based on short-term traffic forecasting" Transportation Research Record: Journal of the Transportation Research Board (2017) 10.3141/2634-10

[20]

Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada.

[21]

Liu "Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction" IEEE Transactions on Intelligent Transportation Systems (2019) 10.1109/tits.2019.2915525

[22]

Lu, B., Gan, X., Zhang, W., Yao, H., Fu, L., & Wang, X. (2022). Spatio-temporal graph few-shot learning with cross-city knowledge transfer. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), Washington, DC (pp. 1162–1172). 10.1145/3534678.3539281

[23]

McNally, M.G. (2000). The four step model. Center for activity systems analysis. https://escholarship.org/uc/item/7j0003j0

[24]

Moreira-Matias "Predicting taxi-passenger demand using streaming data" IEEE Transactions on Intelligent Transportation Systems (2013) 10.1109/tits.2013.2262376

[25]

Mukai "Taxi demand forecasting based on taxi probe data by neural network" (2012)

[26]

Nishikawa, A. (2022). Statistical data and map data of the 289 election districts revised in 2022. (In Japanese). https://gtfs-gis.jp/senkyoku

[27]

NYC Districting Commission. (2022). NYC board of elections 2023 city council election maps. https://www.nyc.gov/site/districting/maps/maps.page

[28]

Peng "Taxi origin and destination demand prediction based on deep learning: A review" Digital Transportation and Safety (2023) 10.48130/dts-2023-0014

[29]

Pereira "FEMa: A finite element machine for fast learning" Neural Computing and Applications (2020) 10.1007/s00521-019-04146-4

[30]

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Xudong Qi, Junfeng Yao, Ping Wang et al.

IET Intelligent Transport Systems 2024 10.1049/itr2.12401

[31]

Qu "Towards real-world traffic prediction and data imputation: A multi-task pretraining and fine-tuning approach" Information Sciences (2024) 10.1016/j.ins.2023.119972

[32]

Qu "ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios" Knowledge-Based Systems (2023) 10.1016/j.knosys.2023.110591

[33]

Rafiei "A new neural dynamic classification algorithm" IEEE Transactions on Neural Networks and Learning Systems (2017) 10.1109/tnnls.2017.2682102

[34]

Self-Supervised Learning for Electroencephalography

Mohammad H. Rafiei, Lynne V. Gauthier, Hojjat Adeli et al.

IEEE Transactions on Neural Networks and Learning... 2024 10.1109/tnnls.2022.3190448

[35]

Rafiei "Self-supervised learning for near-wild cognitive workload estimation" Journal of Medical Systems (2024) 10.1007/s10916-024-02122-7

[36]

Romanowska "Investigating the impact of weather conditions and time of day on traffic flow characteristics" Weather, Climate, and Society (2022) 10.1175/wcas-d-22-0012.1

[37]

Samant "Enhancing neural network incident detection algorithms using wavelets" Computer-Aided Civil and Infrastructure Engineering (2001) 10.1111/0885-9507.00229

[38]

Shi, H., Yao, Q., Guo, Q., Li, Y., Zhang, L., Ye, J., Li, Y., & Liu, Y. (2020). Predicting origin-destination flow via multi-perspective graph convolutional network. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX (pp. 1818–1821). 10.1109/icde48307.2020.00178

[39]

The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

D. I. Shuman, S. K. Narang, P. Frossard et al.

IEEE Signal Processing Magazine 2013 10.1109/msp.2012.2235192

[40]

Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., Ye, J., & Lv, W. (2017). The simpler the better: A unified approach to predicting original taxi demands based on large-scale online platforms. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '23), Halifax, NS, Canada (pp. 1653–1662). 10.1145/3097983.3098018

[41]

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), New York, NY (pp. 6000–6010).

[42]

Short-term traffic forecasting: Where we are and where we’re going

Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias

Transportation Research Part C: Emerging Technolog... 2014 10.1016/j.trc.2014.01.005

[43]

Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., & Zheng, K. (2019). Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’ 19), Anchorage, AK (pp. 1227–11235). 10.1145/3292500.3330877

[44]

Xu "Space-time adaptive network for origin-destination passenger demand prediction" Transportation Research Part C: Emerging Technologies (2024) 10.1016/j.trc.2024.104842

[45]

Xu "Real-time prediction of taxi demand using recurrent neural networks" IEEE Transactions on Intelligent Transportation Systems (2018) 10.1109/tits.2017.2755684

[46]

Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.-J., & Xiong, H. (2020). Spatial-Temporal Transformer Networks for Traffic Flow Forecasting. arXiv. https://doi.org/10.48550/ARXIV.2001.02908

[47]

Yang, Y., Zhang, S., Zhang, C., & Yu, J.J.Q. (2021). Origin-destination matrix prediction via hexagon-based generated graph. Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN (pp. 1399–1404). 10.1109/itsc48978.2021.9564718

[48]

Yao, H., Liu, Y., Wei, Y., Tang, X., & Li, Z. (2019). Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. Proceedings of the 2019 World Wide Web Conference (WWW’19), San Francisco, CA (pp. 2181–2191). 10.1145/3308558.3313577

[49]

Yao "Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks" IEEE Transactions on Intelligent Transportation Systems (2021) 10.1109/tits.2020.3003310

[50]

Ye "Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents" Expert Systems with Applications (2023) 10.1016/j.eswa.2023.121101

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Published: Jul 01, 2025
Vol/Issue: 40(17)
Pages: 2498-2521
License: View

Authors

Faculty of Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong

Y

Yiyang Wang

Architecture and Design College, Nanchang University, Nanchang, China

College of Artificial Intelligence, Nankai University, Tianjin, China

Cite This Article

Zeren Xing, Edward Chung, Yiyang Wang, et al. (2025). Origin–destination prediction via knowledge-enhanced hybrid learning. Computer-Aided Civil and Infrastructure Engineering, 40(17), 2498-2521. https://doi.org/10.1111/mice.13458

Origin–destination prediction via knowledge-enhanced hybrid learning

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