The Geospatial Generalization Problem: When Mobility Isn't Mobile
Article No.: 47, Pages 1 - 4
Abstract
Human mobility research has significantly benefited from recent advances in machine learning, as have numerous other industries. Aided by the ever-increasing availability of geospatial and mobility data, machine learning models have enabled large-scale systems for simulating city-wide macro and micro mobility behaviors, urban planning, transportation management, and disaster relief optimization. However, while many fields have invested significant effort in solving the model transferability and generalization problem, the inability of machine learning-based human mobility models to generalize to new locations has come to be implicitly accepted in most geospatial research. In this vision paper, we focus on this geospatial generalization problem, its root causes, and how it is restricting the applications of otherwise-promising research. Most importantly, we argue for several data- and modeling-driven innovations which could help remedy this problem, spanning mega-scale simulations, large foundation models, and multi-task, transfer, and meta-learning. We also spotlight a handful of promising ideas which have recently emerged from the community. We hope that these proposals take root and help develop more capable, flexible, and generalizable models in research and industry.
References
[1]
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, and Kai Nagel. 2009. MATSim-T: Architecture and simulation times. In Multiagent systems for traffic and transportation engineering. IGI Global, 57--78.
[2]
Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, and Xiaowei Jia. 2022. STORMGAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19. In 2022 IEEE Intl. Conf. on Data Mining (ICDM). IEEE.
[3]
Marco Bonola, Lorenzo Bracciale, Pierpaolo Loreti, Raul Amici, Antonello Rabuffi, and Giuseppe Bianchi. 2016. Opportunistic communication in smart city: Experimental insight with small-scale taxi fleets as data carriers. Ad Hoc Networks 43 (2016), 43--55.
[4]
Lorenzo Bracciale, Marco Bonola, Pierpaolo Loreti, Giuseppe Bianchi, Raul Amici, and Antonello Rabuffi. 2014. CRAWDAD dataset roma/taxi (v. 2014-07-17).
[5]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877--1901.
[6]
Yan Chen, Jingjing Gu, Fuzhen Zhuang, Xinjiang Lu, and Ming Sun. 2022. Exploiting Hierarchical Correlations for Cross-City Cross-Mode Traffic Flow Prediction. In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 891--896.
[7]
Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel. 2013. Unique in the crowd: The privacy bounds of human mobility. Scientific reports 3, 1 (2013), 1--5.
[8]
Ziquan Fang, Dongen Wu, Lu Pan, et al. 2022. When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters. IJCAI'22 (2022), 2030--2036.
[9]
Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco Pereira, and Marco Pavone. 2022. Graph meta-reinforcement learning for transferable autonomous mobility-on-demand. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2913--2923.
[10]
Dedre Gentner and Christian Hoyos. 2017. Analogy and abstraction. Topics in cognitive science 9, 3 (2017), 672--693.
[11]
Stefano Ghirlanda and Magnus Enquist. 2003. A century of generalization. Animal Behaviour 66, 1 (2003), 15--36.
[12]
Brian Y He, Jinkai Zhou, Ziyi Ma, Joseph YJ Chow, and Kaan Ozbay. 2020. Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population. Transportation Research Part A: Policy and Practice 141 (2020), 444--467.
[13]
Brian Yueshuai He, Jinkai Zhou, Ziyi Ma, Ding Wang, Di Sha, Mina Lee, Joseph YJ Chow, and Kaan Ozbay. 2021. A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City. Transport Policy 101 (2021), 145--161.
[14]
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2021. Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence 44, 9 (2021), 5149--5169.
[15]
Weiwei Jiang and Jiayun Luo. 2022. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications (2022), 117921.
[16]
Yilun Jin, Kai Chen, and Qiang Yang. 2022. Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 731--741.
[17]
Zekun Li, Jina Kim, Yao-Yi Chiang, and Muhao Chen. 2022. SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation. In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 2757--2769.
[18]
Xiao Liang, Dan Nguyen, and Steve B Jiang. 2020. Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion. Machine Learning: Science and Technology 2, 1 (dec 2020), 015007.
[19]
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021. A survey on deep learning for human mobility. Comput. Surveys 55, 1 (2021).
[20]
Gengchen Mai, Weiming Huang, Jin Sun, et al. 2023. On the opportunities and challenges of foundation models for geospatial artificial intelligence. arXiv preprint arXiv:2304.06798 (2023).
[21]
Tanwi Mallick, Prasanna Balaprakash, Eric Rask, and Jane Macfarlane. 2021. Transfer learning with graph neural networks for short-term highway traffic forecasting. In 2020 25th Intl. Conf. on Pattern Recognition (ICPR). IEEE.
[22]
Luis Moreira-Matias, Joao Gama, Michel Ferreira, Joao Mendes-Moreira, and Luis Damas. 2013. Predicting taxi-passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems 14, 3 (2013), 1393--1402.
[23]
Michal Piorkowski, Natasa Sarafijanovic-Djukic, and Matthias Grossglauser. 2009. CRAWDAD dataset epfl/mobility (v. 2009-02-24).
[24]
Ashutosh Sao, Simon Gottschalk, Nicolas Tempelmeier, and Elena Demidova. 2023. MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks. In Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25--28, 2023, Proceedings, Part IV. Springer, 70--82.
[25]
Chujie Tian, Xinning Zhu, Zheng Hu, and Jian Ma. 2021. A transfer approach with attention reptile method and long-term generation mechanism for few-shot traffic prediction. Neurocomputing 452 (2021), 15--27.
[26]
Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, and Yuchen Fang. 2022. Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning. IEEE Transactions on Intelligent Transportation Systems 23, 9 (2022), 15716--15728.
[27]
Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2018. Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386 (2018).
[28]
Senzhang Wang, Jiannong Cao, and S Yu Philip. 2020. Deep learning for spatiotemporal data mining: A survey. IEEE transactions on knowledge and data engineering 34, 8 (2020), 3681--3700.
[29]
Senzhang Wang, Hao Miao, Jiyue Li, and Jiannong Cao. 2021. Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks. IEEE Transactions on Intelligent Transportation Systems 23, 5 (2021).
[30]
Yuan Wang, Dongxiang Zhang, Ying Liu, Bo Dai, and Loo Hay Lee. 2019. Enhancing transportation systems via deep learning: A survey. Transportation research part C: emerging technologies 99 (2019), 144--163.
[31]
Hao Xue, Bhanu Prakash Voutharoja, and Flora D Salim. 2022. Leveraging language foundation models for human mobility forecasting. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems.
[32]
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In The World Wide Web Conference. 2181--2191.
[33]
Jing Yuan, Yu Zheng, Chengyang Zhang, et al. 2010. T-Drive: Driving Directions Based on Taxi Trajectories. In Proceedings of 18th ACM SIGSPATIAL.
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Published In
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
- General Chairs:
- Maria Luisa Damiani,
- Matthias Renz,
- Program Chairs:
- Ahmed Eldawy,
- Peer Kröger,
- Mario A Nascimento
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Published: 22 December 2023
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SIGSPATIAL '23
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SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems
November 13 - 16, 2023
Hamburg, Germany
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