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Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

Published: 27 April 2024 Publication History

Abstract

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic. In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art.

References

[1]
Essam Algizawy, Tetsuji Ogawa, and Ahmed El-Mahdy. 2017. Real-time large-scale map matching using mobile phone data. ACM Transactions on Knowledge Discovery from Data (TKDD) 11, 4 (2017), 1–38.
[2]
Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte R. James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José J. Ramasco, Filippo Simini, and Marcello Tomasini. 2018. Human mobility: Models and applications. Physics Reports 734 (2018), 1–74.
[3]
Marc Barthélemy. 2011. Spatial networks. Physics Reports 499, 1-3 (2011), 1–101.
[4]
Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. 2011. SUMO–simulation of urban mobility: An overview. In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind.
[5]
Yanguang Chen. 2015. The distance-decay function of geographical gravity model: Power law or exponential law? Chaos, Solitons & Fractals 77 (2015), 174–189.
[6]
Shaojie Dai, Jinshuai Wang, Chao Huang, Yanwei Yu, and Junyu Dong. 2022. Dynamic multi-view graph neural networks for citywide traffic inference. ACM Transactions on Knowledge Discovery from Data (TKDD) (2022).
[7]
Jacob J. De Vries, Peter Nijkamp, and Piet Rietveld. 2009. Exponential or power distance-decay for commuting? An alternative specification. Environment and Planning A 41, 2 (2009), 461–480.
[8]
Jinliang Deng, Xiusi Chen, Zipei Fan, Renhe Jiang, Xuan Song, and Ivor W. Tsang. 2021. The pulse of urban transport: Exploring the co-evolving pattern for spatio-temporal forecasting. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 6 (2021), 1–25.
[9]
Jie Feng, Yong Li, Ziqian Lin, Can Rong, Funing Sun, Diansheng Guo, and Depeng Jin. 2021. Context-aware spatial-temporal neural network for citywide crowd flow prediction via modeling long-range spatial dependency. ACM Transactions on Knowledge Discovery from Data (TKDD) 16, 3 (2021), 1–21.
[10]
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. DeepMove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference. 1459–1468.
[11]
A. Stewart Fotheringham. 1981. Spatial structure and distance-decay parameters. Annals of the Association of American Geographers 71, 3 (1981), 425–436.
[12]
Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. 2019. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 3656–3663.
[13]
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922–929.
[14]
Renjun Hu, Yanchi Liu, Yanyan Li, Jingbo Zhou, Shuai Ma, and Hui Xiong. 2020. Exploiting user preference and mobile peer influence for human mobility annotation. ACM Transactions on Knowledge Discovery from Data (TKDD) 14, 6 (2020), 1–18.
[15]
Md. Shahadat Iqbal, Charisma F. Choudhury, Pu Wang, and Marta C. González. 2014. Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies 40 (2014), 63–74.
[16]
Sourabh Jain, Sukhvir Singh Jain, and Gaurav Jain. 2017. Traffic congestion modelling based on origin and destination. Procedia Engineering 187 (2017), 442–450.
[17]
Hyunmyung Kim, Seungkirl Baek, and Yongtaek Lim. 2001. Origin-destination matrices estimated with a genetic algorithm from link traffic counts. Transportation Research Record 1771, 1 (2001), 156–163.
[18]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[19]
Maxime Lenormand, Aleix Bassolas, and José J. Ramasco. 2016. Systematic comparison of trip distribution laws and models. Journal of Transport Geography 51 (2016), 158–169.
[20]
Maxime Lenormand, Sylvie Huet, and Floriana Gargiulo. 2014. Generating French virtual commuting networks at the municipality level. Journal of Transport and Land Use 7, 1 (2014), 43–55.
[21]
Baibing Li. 2005. Bayesian inference for origin-destination matrices of transport networks using the EM algorithm. Technometrics 47, 4 (2005), 399–408.
[22]
Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. 2021. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data (TKDD) (2021).
[23]
Jiachen Li, Fan Yang, Masayoshi Tomizuka, and Chiho Choi. 2020. EvolveGraph: Multi-agent trajectory prediction with dynamic relational reasoning. Advances in Neural Information Processing Systems 33 (2020), 19783–19794.
[24]
Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, and Fei Wu. 2018. Deep sequence learning with auxiliary information for traffic prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 537–546.
[25]
Fandel Lin and Hsun-Ping Hsieh. 2021. A joint passenger flow inference and path recommender system for deploying new routes and stations of mass transit transportation. ACM Transactions on Knowledge Discovery from Data 16, 1 (2021), 1–36.
[26]
Shuai Ling, Zhe Yu, Shaosheng Cao, Haipeng Zhang, and Simon Hu. 2022. STHAN: Transportation demand forecasting with compound spatio-temporal relationships. ACM Transactions on Knowledge Discovery from Data (TKDD) (2022).
[27]
Yu Liu, Jingtao Ding, Yanjie Fu, and Yong Li. 2023. UrbanKG: An urban knowledge graph system. ACM Transactions on Intelligent Systems and Technology 14, 4 (2023), 1–25.
[28]
Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, and Claudio Silva. 2020. Learning geo-contextual embeddings for commuting flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 808–816.
[29]
Chung-Cheng Lu, Xuesong Zhou, and Kuilin Zhang. 2013. Dynamic origin–destination demand flow estimation under congested traffic conditions. Transportation Research Part C: Emerging Technologies 34 (2013), 16–37.
[30]
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021. A survey on deep learning for human mobility. ACM Computing Surveys (CSUR) 55, 1 (2021), 1–44.
[31]
Nastaran Pourebrahim, Selima Sultana, Amirreza Niakanlahiji, and Jean-Claude Thill. 2019. Trip distribution modeling with Twitter data. Computers, Environment and Urban Systems 77 (2019), 101354.
[32]
Caleb Robinson and Bistra Dilkina. 2018. A machine learning approach to modeling human migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. 1–8.
[33]
Can Rong, Jingtao Ding, and Yong Li. 2023. An interdisciplinary survey on origin-destination flows modeling: Theory and techniques. arXiv preprint arXiv:2306.10048 (2023).
[34]
Can Rong, Jingtao Ding, Zhicheng Liu, and Yong Li. 2023. City-wide origin-destination matrix generation via graph denoising diffusion. arXiv preprint arXiv:2306.04873 (2023).
[35]
Can Rong, Jie Feng, and Yong Li. 2019. Deep learning models for population flow generation from aggregated mobility data. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 1008–1013.
[36]
Can Rong, Tong Li, Jie Feng, Hedong Yang, Lu Geng, and Yong Li. 2021. Inferring origin-destination flows from population distribution. IEEE Transactions on Knowledge and Data Engineering (2021).
[37]
Aurore Sallard, Miloš Balać, and Sebastian Hörl. 2021. An open data-driven approach for travel demand synthesis: An application to São Paulo. Regional Studies, Regional Science 8, 1 (2021), 371–386.
[38]
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia. 2020. Learning to simulate complex physics with graph networks. In International Conference on Machine Learning. PMLR, 8459–8468.
[39]
Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, and Yan Liu. 2020. Predicting origin-destination flow via multi-perspective graph convolutional network. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1818–1821.
[40]
Filippo Simini, Gianni Barlacchi, Massimilano Luca, and Luca Pappalardo. 2021. A deep gravity model for mobility flows generation. Nature Communications 12, 1 (2021), 1–13.
[41]
Filippo Simini, Marta C. González, Amos Maritan, and Albert-László Barabási. 2012. A universal model for mobility and migration patterns. Nature 484, 7392 (2012), 96–100.
[42]
Kay W. Axhausen, Andreas Horni, and Kai Nagel. 2016. The Multi-agent Transport Simulation MATSim. Ubiquity Press.
[43]
Chunnan Wang, Kaixin Zhang, Hongzhi Wang, and Bozhou Chen. 2022. Auto-STGCN: Autonomous spatial-temporal graph convolutional network search. ACM Transactions on Knowledge Discovery from Data (2022).
[44]
Minjie Yu Wang. 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
[45]
Yuandong Wang, Xuelian Lin, Hua Wei, Tianyu Wo, Zhou Huang, Yong Zhang, and Jie Xu. 2019. A unified framework with multi-source data for predicting passenger demands of ride services. ACM Transactions on Knowledge Discovery from Data (TKDD) 13, 6 (2019), 1–24.
[46]
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019).
[47]
Keli Xiao, Zeyang Ye, Lihao Zhang, Wenjun Zhou, Yong Ge, and Yuefan Deng. 2020. Multi-user mobile sequential recommendation for route optimization. ACM Transactions on Knowledge Discovery from Data (TKDD) 14, 5 (2020), 1–28.
[48]
Yanan Xu, Yanmin Zhu, Yanyan Shen, and Jiadi Yu. 2019. Fine-grained air quality inference with remote sensing data and ubiquitous urban data. ACM Transactions on Knowledge Discovery from Data (TKDD) 13, 5 (2019), 1–27.
[49]
Hai Yang. 1995. Heuristic algorithms for the bilevel origin-destination matrix estimation problem. Transportation Research Part B: Methodological 29, 4 (1995), 231–242.
[50]
Hai Yang, Tsuna Sasaki, Yasunori Iida, and Yasuo Asakura. 1992. Estimation of origin-destination matrices from link traffic counts on congested networks. Transportation Research Part B: Methodological 26, 6 (1992), 417–434.
[51]
Mogeng Yin, Madeleine Sheehan, Sidney Feygin, Jean-François Paiement, and Alexei Pozdnoukhov. 2017. A generative model of urban activities from cellular data. IEEE Transactions on Intelligent Transportation Systems 19, 6 (2017), 1682–1696.
[52]
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017).
[53]
Guanjie Zheng, Chang Liu, Hua Wei, Chacha Chen, and Zhenhui Li. 2021. Rebuilding city-wide traffic origin destination from road speed data. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 301–312.
[54]
Zhilun Zhou, Jingtao Ding, Yu Liu, Depeng Jin, and Yong Li. 2023. Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. 1–12.
[55]
Yi Zhu, Mi Diao, Joseph Ferreira, and P. Christopher Zegras. 2018. An integrated microsimulation approach to land-use and mobility modeling. Journal of Transport and Land Use 11, 1 (2018), 633–659.
[56]
George Kingsley Zipf. 1946. The P 1 P 2/D hypothesis: On the intercity movement of persons. American Sociological Review 11, 6 (1946), 677–686.

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  • (2024)Controllable Human Trajectory Generation Using Profile-Guided Latent DiffusionACM Transactions on Knowledge Discovery from Data10.1145/370173619:1(1-25)Online publication date: 25-Oct-2024

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  1. Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

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    cover image ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data  Volume 18, Issue 6
    July 2024
    760 pages
    EISSN:1556-472X
    DOI:10.1145/3613684
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 April 2024
    Online AM: 20 February 2024
    Accepted: 16 January 2024
    Revised: 06 November 2023
    Received: 09 March 2023
    Published in TKDD Volume 18, Issue 6

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    Author Tags

    1. Urban mobility
    2. origin-destination
    3. traffic flow
    4. spatiotemporal graph learning

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    • (2024)Controllable Human Trajectory Generation Using Profile-Guided Latent DiffusionACM Transactions on Knowledge Discovery from Data10.1145/370173619:1(1-25)Online publication date: 25-Oct-2024

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