{"type":"ScholarlyArticle","title":"Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator","authors":[{"name":"Qiang Gao","slug":"qiang-gao","given":"Qiang","family":"Gao","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Xiaolong Song","slug":"xiaolong-song","given":"Xiaolong","family":"Song","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Li Huang","slug":"li-huang","given":"Li","family":"Huang","affiliation":"Southwestern University of Finance and Economics , Chengdu , China, 611130"},{"name":"Goce Trajcevski","slug":"goce-trajcevski","given":"Goce","family":"Trajcevski","affiliation":"Iowa State University , Iowa , USA"},{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":"University of Electronic Science and Technology of China , Chengdu , China, 610054"},{"name":"Xueqin Chen","slug":"xueqin-chen","given":"Xueqin","family":"Chen","affiliation":"Delft University of Technology , Delft , Netherlands , 2628CN"}],"abstract":"Fine-grained urban flow inference (FUFI), which involves inferring fine-grained flow maps from their coarse-grained counterparts, is of tremendous interest in the realm of sustainable urban traffic services. To address the FUFI, existing solutions mainly concentrate on investigating spatial dependencies, introducing external factors, reducing excessive memory costs, etc., -- while rarely considering the catastrophic forgetting (CF) problem. Motivated by recent operator learning, we present an Urban Neural Operator solution with Incremental learning (UNOI), primarily seeking to learn grained-invariant solutions for FUFI in addition to addressing CF. Specifically, we devise an urban neural operator (UNO) in UNOI that learns mappings between approximation spaces by treating the different-grained flows as continuous functions, allowing a more flexible capture of spatial correlations. Furthermore, the phenomenon of CF behind time-related flows could hinder the capture of flow dynamics. Thus, UNOI mitigates CF concerns as well as privacy issues by placing UNO blocks in two incremental settings, i.e., flow-related and task-related. Experimental results on large-scale real-world datasets demonstrate the superiority of our proposed solution against the baselines.","doi":"10.24963/ijcai.2024/644","url":"https://ijcai.org/proceedings/2024/644","pdf":"https://ijcai.org/proceedings/2024/0644.pdf","markdown":"https://ijcai.org/proceedings/2024/644.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/644","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":644,"pages":{"first":5826,"last":5834},"keywords":["Data Mining: DM: Applications","Data Mining: DM: Mining spatial and/or temporal data","Multidisciplinary Topics and Applications: MTA: Transportation","Multidisciplinary Topics and Applications: MTA: Sensor networks and smart cities"],"videos":[],"references":[{"position":1,"raw":"[Buzzega et al., 2020] Pietro Buzzega, Matteo Boschini, An- gelo Porrello, Davide Abati, and Simone Calderara. Dark experience for general continual learning: a strong, simple baseline. In Proceedings of the 34th International Con- ference on Neural Information Processing Systems, pages 15920-15930, 2020.","title":"Dark experience for general continual learning: a strong, simple baseline","authors":"Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara","year":"2020","venue":"Proceedings of the 34th International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cao, 2021] Shuhao Cao. Choose a transformer: Fourier or galerkin. In 35th Conference on Neural Information Pro- cessing Systems (NeurIPS 2021), 2021.","title":"Choose a transformer: Fourier or galerkin","authors":"Shuhao Cao, Cao","year":"2021","venue":"35th Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2019] Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, and Shengyu Zhang. Rethinking the usage of batch normalization and dropout in the training of deep neural networks. arXiv preprint arXiv:1905.05928, 2019.","title":"Rethinking the usage of batch normalization and dropout in the training of deep neural networks","authors":"Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, Shengyu Zhang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dong et al., 2015] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2):295-307, 2015.","title":"Image super-resolution using deep convolutional networks","authors":"Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang","year":"2015","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao et al., 2023] Qiang Gao, Xiaojun Shan, Yuchen Zhang, and Fan Zhou. Enhancing knowledge transfer for task in- cremental learning with data-free subnetwork. In Thirty- seventh Conference on Neural Information Processing Systems, 2023.","title":"Enhancing knowledge transfer for task incremental learning with data-free subnetwork","authors":"Qiang Gao, Xiaojun Shan, Yuchen Zhang, Fan Zhou","year":"2023","venue":"Thirtyseventh Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Greenberg, 2015] Michael D Greenberg. Applications of Green's functions in science and engineering. Courier Dover Publications, 2015.","title":"Applications of Green's functions in science and engineering","authors":"D Michael, Greenberg","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hershberger, 2013] John Hershberger. Stable snap round- ing. Comput. Geom., 46(4):403-416, 2013.","title":"Stable snap rounding","authors":"John Hershberger","year":"2013","venue":"Comput. Geom","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kim et al., 2016] Jiwon Kim, Jung Kwon Lee, and Ky- oung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1646-1654, 2016.","title":"Accurate image super-resolution using very deep convolutional networks","authors":"Jiwon Kim, Jung Kwon Lee, Kyoung Mu, Lee","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kirkpatrick et al., 2017] James Kirkpatrick, Razvan Pas- canu, Neil Rabinowitz, Joel Veness, Guillaume Des- jardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcom- ing catastrophic forgetting in neural networks. Proceed- ings of the national academy of sciences, 114(13):3521- 3526, 2017.","title":"Overcoming catastrophic forgetting in neural networks","authors":"James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska","year":"2017","venue":"Proceedings of the national academy of sciences","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kovachki et al., 2021] Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhat- tacharya, Andrew Stuart, and Anima Anandkumar. Neural operator: Learning maps between function spaces. arXiv preprint arXiv:2108.08481, 2021.","title":"Neural operator: Learning maps between function spaces","authors":"Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kovachki et al., 2023] Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhat- tacharya, Andrew Stuart, and Anima Anandkumar. Neural operator: Learning maps between function spaces with applications to pdes. Journal of Machine Learning Research, 24(89):1-97, 2023.","title":"Neural operator: Learning maps between function spaces with applications to pdes","authors":"Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar","year":"2023","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ledig et al., 2017] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super- resolution using a generative adversarial network. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 4681-4690, 2017.","title":"Photo-realistic single image superresolution using a generative adversarial network","authors":"Ledig","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2020a] Zongyi Li, Nikola Kovachki, Kamyar Az- izzadenesheli, Burigede Liu, Kaushik Bhattacharya, An- drew Stuart, and Anima Anandkumar. Fourier neural op- erator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020.","title":"Fourier neural operator for parametric partial differential equations","authors":"Li","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2020b] Zongyi Li, Nikola Kovachki, Kamyar Az- izzadenesheli, Burigede Liu, Kaushik Bhattacharya, An- drew Stuart, and Anima Anandkumar. Multipole graph neural operator for parametric partial differential equa- tions. In Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020.","title":"Multipole graph neural operator for parametric partial differential equations","authors":"Li","year":"2020","venue":"Proceedings of the 34th International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2020c] Zongyi Li, Nikola Kovachki, Kamyar Az- izzadenesheli, Burigede Liu, Kaushik Bhattacharya, An- drew Stuart, and Anima Anandkumar. Neural operator: Graph kernel network for partial differential equations. arXiv preprint arXiv:2003.03485, 2020.","title":"Neural operator: Graph kernel network for partial differential equations","authors":"Li","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2022] Zijie Li, Kazem Meidani, and Amir Barati Farimani. Transformer for partial differential equations' operator learning. arXiv preprint arXiv:2205.13671, 2022.","title":"Transformer for partial differential equations' operator learning","authors":"Li","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liang et al., 2019] Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, and Yu Zheng. Urbanfm: Inferring fine-grained urban flows. In Proceedings of the 25th ACM SIGKDD interna- tional conference on knowledge discovery and data min- ing, page 3132-3142, 2019.","title":"Urbanfm: Inferring fine-grained urban flows","authors":"Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, Yu Zheng","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liang et al., 2020] Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S. Rosenblum. Revisiting convolutional neural networks for citywide crowd flow analytics. In The European Confer- ence on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.","title":"Revisiting convolutional neural networks for citywide crowd flow analytics","authors":"Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, David S Rosenblum","year":"2020","venue":"The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liang et al., 2021] Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosen- blum, and Roger Zimmermann. Fine-grained urban flow prediction. In Proceedings of the Web Conference 2021, pages 1833-1845, 2021.","title":"Fine-grained urban flow prediction","authors":"Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosenblum, Roger Zimmermann","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liang et al., 2022] Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S Rosenblum, and Roger Zim- mermann. Mixed-order relation-aware recurrent neural networks for spatio-temporal forecasting. IEEE Transac- tions on Knowledge and Data Engineering, 2022.","title":"Mixed-order relation-aware recurrent neural networks for spatio-temporal forecasting","authors":"Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S Rosenblum, Roger Zimmermann","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lim et al., 2017] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep resid- ual networks for single image super-resolution. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 136-144, 2017.","title":"Enhanced deep residual networks for single image super-resolution","authors":"Lim","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lopez-Paz and Ranzato, 2017] David Lopez-Paz and Marc'Aurelio Ranzato. Gradient episodic memory for continual learning. In Proceedings of the 31st Inter- national Conference on Neural Information Processing Systems, pages 6470-6479, 2017.","title":"Gradient episodic memory for continual learning","authors":"David Lopez, - Paz, Marc'aurelio Ranzato","year":"2017","venue":"Proceedings of the 31st International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Nayak et al., 2019] Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. Zero-shot knowledge distilla- tion in deep networks. In International Conference on Ma- chine Learning, pages 4743-4751. PMLR, 2019.","title":"Zero-shot knowledge distillation in deep networks","authors":"Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty","year":"2019","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ouyang et al., 2020] Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, and David S Rosen- blum. Fine-grained urban flow inference. IEEE trans- actions on knowledge and data engineering, 34(6):2755- 2770, 2020.","title":"Fine-grained urban flow inference","authors":"Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, David S Rosenblum","year":"2020","venue":"IEEE transactions on knowledge and data engineering","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Reddy, 2013] Junuthula Narasimha Reddy. An introduction to the finite element method, volume 3. McGraw-Hill New York, 2013.","title":"An introduction to the finite element method","authors":"Junuthula Narasimha, Reddy","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shi et al., 2016] Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single im- age and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1874-1883, 2016.","title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","authors":"Shi","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Troisemaine et al., 2023] Colin Troisemaine, Vincent Lemaire, Stéphane Gosselin, Alexandre Reiffers-Masson, Joachim Flocon-Cholet, and Sandrine Vaton. Novel class discovery: an introduction and key concepts. arXiv preprint arXiv:2302.12028, 2023.","title":"Novel class discovery: an introduction and key concepts","authors":"Colin Troisemaine, Vincent Lemaire, Stéphane Gosselin, Alexandre Reiffers-Masson, Joachim Flocon-Cholet, Sandrine Vaton","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Vandal et al., 2017] Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, and Auroop R Ganguly. Deepsd: Generating high resolution climate change projections through single image super- resolution. In Proceedings of the 23rd ACM SIGKDD in- ternational conference on knowledge discovery and data mining, pages 1663-1672, 2017.","title":"Deepsd: Generating high resolution climate change projections through single image superresolution","authors":"Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, Auroop R Ganguly","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Ad- vances in Neural Information Processing Systems, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł Ukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wei and Zhang, 2023] Min Wei and Xuesong Zhang. Super-resolution neural operator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18247-18256, 2023.","title":"Super-resolution neural operator","authors":"Zhang ; ] Wei, Min Wei, Xuesong Zhang","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yu et al., 2023] Haoyang Yu, Xovee Xu, Ting Zhong, and Fan Zhou. Overcoming forgetting in fine-grained urban flow inference via adaptive knowledge replay. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, 2023.","title":"Overcoming forgetting in fine-grained urban flow inference via adaptive knowledge replay","authors":"Yu","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zheng et al., 2014] Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. Urban computing: concepts, methodolo- gies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3):1-55, 2014.","title":"Urban computing: concepts, methodologies, and applications","authors":"Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang","year":"2014","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhou et al., 2020] Fan Zhou, Liang Li, Ting Zhong, Goce Trajcevski, Kunpeng Zhang, and Jiahao Wang. Enhancing urban flow maps via neural odes. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020.","title":"Enhancing urban flow maps via neural odes","authors":"Fan Zhou, Liang Li, Ting Zhong, Goce Trajcevski, Kunpeng Zhang, Jiahao Wang","year":"2020","venue":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/180"},{"position":34,"raw":"[Zhou et al., 2021] Fan Zhou, Xin Jing, Liang Li, and Ting Zhong. Inferring high-resolutional urban flow with inter- net of mobile things. In ICASSP 2021-2021 IEEE Inter- national Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pages 7948-7952. IEEE, 2021.","title":"Inferring high-resolutional urban flow with internet of mobile things","authors":"Fan Zhou, Xin Jing, Liang Li, Ting Zhong","year":"2021","venue":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}