{"type":"ScholarlyArticle","title":"Predicting Carpark Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach","authors":[{"name":"Huaiwu Zhang","slug":"huaiwu-zhang","given":"Huaiwu","family":"Zhang","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Yutong Xia","slug":"yutong-xia","given":"Yutong","family":"Xia","affiliation":"National University of Singapore , Singapore"},{"name":"Siru Zhong","slug":"siru-zhong","given":"Siru","family":"Zhong","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , China"},{"name":"Kun Wang","slug":"kun-wang","given":"Kun","family":"Wang","affiliation":"National University of Singapore , Singapore"},{"name":"Zekun Tong","slug":"zekun-tong","given":"Zekun","family":"Tong","affiliation":"National University of Singapore , Singapore"},{"name":"Qingsong Wen","slug":"qingsong-wen","given":"Qingsong","family":"Wen","affiliation":"Squirrel AI , USA"},{"name":"Roger Zimmermann","slug":"roger-zimmermann","given":"Roger","family":"Zimmermann","affiliation":"National University of Singapore , Singapore"},{"name":"Yuxuan Liang","slug":"yuxuan-liang","given":"Yuxuan","family":"Liang","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , China; State Key Laboratory of Resources and Environmental Information System , China"}],"abstract":"The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. The contributions in this paper are listed as follows: (1) A New Dataset: We introduce the SINPA dataset, containing a year's worth of PA data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors. (2) A Data-Driven Approach: We present DeepPA, a novel deep-learning framework, to collectively and efficiently predict future PA across thousands of parking lots. (3) Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Furthermore, we implement DeepPA in a practical web-based platform to provide real-time PA predictions to aid drivers and inform urban planning for the governors in Singapore. We release the dataset and source code at https://github.com/yoshall/SINPA.","doi":"10.24963/ijcai.2024/836","url":"https://ijcai.org/proceedings/2024/836","pdf":"https://ijcai.org/proceedings/2024/0836.pdf","markdown":"https://ijcai.org/proceedings/2024/836.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/836","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":"AI for Good","track":"AI for Good","number":836,"pages":{"first":7554,"last":7562},"keywords":["Machine Learning: General","Data Mining: General"],"videos":[],"references":[{"position":1,"raw":"[Badii et al., 2018] Claudio Badii, Paolo Nesi, and Irene Paoli. Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access, 6:44059-44071, 2018.","title":"Predicting available parking slots on critical and regular services by exploiting a range of open data","authors":"Claudio Badii, Paolo Nesi, Irene Paoli","year":"2018","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2018] Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolu- tional and recurrent networks for sequence modeling. arXiv:1803.01271, 2018.","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","authors":"J Zico Shaojie Bai, Vladlen Kolter, Koltun","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bai et al., 2020] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural infor- mation processing systems, 33:17804-17815, 2020.","title":"Adaptive graph convolutional recurrent network for traffic forecasting","authors":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Choi et al., 2022] Jeongwhan Choi, Hwangyong Choi, Jee- hyun Hwang, and Noseong Park. Graph neural controlled differential equations for traffic forecasting. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 36, pages 6367-6374, 2022.","title":"Graph neural controlled differential equations for traffic forecasting","authors":"Choi","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chung et al., 2014] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evalua- tion of gated recurrent neural networks on sequence mod- eling. arXiv preprint arXiv:1412.3555, 2014.","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","authors":"Chung","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fang et al., 2021] Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie. Spatial-temporal graph ode net- works for traffic flow forecasting. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 364-373, 2021.","title":"Spatial-temporal graph ode networks for traffic flow forecasting","authors":"Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Geng et al., 2019] Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. Spatiotemporal multi-graph convolution network for ride- hailing demand forecasting. In AAAI, volume 33, pages 3656-3663, 2019.","title":"Spatiotemporal multi-graph convolution network for ridehailing demand forecasting","authors":"Yaguang Xu Geng, Leye Li, Lingyu Wang, Qiang Zhang, Jieping Yang, Yan Ye, Liu","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Graves, 2013] Alex Graves. Generating sequences with re- current neural networks. arXiv preprint arXiv:1308.0850, 2013.","title":"Generating sequences with recurrent neural networks","authors":"Alex Graves","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Guibas et al., 2021] John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, and Bryan Catan- zaro. Efficient token mixing for transformers via adaptive fourier neural operators. In International Conference on Learning Representations, 2021.","title":"Efficient token mixing for transformers via adaptive fourier neural operators","authors":"John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro","year":"2021","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Guo et al., 2019] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based spatial- temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 922-929, 2019.","title":"Attention based spatialtemporal graph convolutional networks for traffic flow forecasting","authors":"Guo","year":"2019","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jean et al., 2016] Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon. Combining satellite imagery and machine learning to pre- dict poverty. Science, 353(6301):790-794, 2016.","title":"Combining satellite imagery and machine learning to predict poverty","authors":"Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David B Lobell, Stefano Ermon","year":"2016","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jin et al., 2023] Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng. Spatio-temporal graph neural networks for predictive learning in urban computing: A survey. IEEE Transac- tions on Knowledge and Data Engineering, 2023.","title":"Spatio-temporal graph neural networks for predictive learning in urban computing: A survey","authors":"Jin","year":"2023","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kelly and Fussell, 2015] Frank J Kelly and Julia C Fussell. Air pollution and public health: emerging hazards and im- proved understanding of risk. Environmental geochemistry and health, 37:631-649, 2015.","title":"Air pollution and public health: emerging hazards and improved understanding of risk","authors":"J Frank, Julia C Kelly, Fussell","year":"2015","venue":"Environmental geochemistry and health","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"Kipf and Welling, ] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Rep- resentations.","title":"Semi-supervised classification with graph convolutional networks","authors":"Welling Kipf, ] Thomas, N Kipf, Max Welling","year":null,"venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Koren, 2003] Yehuda Koren. On spectral graph drawing. In International Computing and Combinatorics Conference, pages 496-508. Springer, 2003.","title":"On spectral graph drawing","authors":"Koren ;, Yehuda Koren","year":"2003","venue":"International Computing and Combinatorics Conference","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2018] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural net- work: Data-driven traffic forecasting. In International Conference on Learning Representations (ICLR '18), 2018.","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","authors":"Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu","year":"2018","venue":"International Conference on Learning Representations (ICLR '18)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2020] 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":"Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liang et al., 2017] Yuxuan Liang, Zhongyuan Jiang, and Yu Zheng. Inferring traffic cascading patterns. In Proceed- ings of the 25th acm sigspatial international conference on advances in geographic information systems, pages 1-10, 2017.","title":"Inferring traffic cascading patterns","authors":"Yuxuan Liang, Zhongyuan Jiang, Yu Zheng","year":"2017","venue":"Proceedings of the 25th acm sigspatial international conference on advances in geographic information systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liang et al., 2018] Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng. Geoman: Multi-level attention networks for geo-sensory time series prediction. In IJCAI, 2018.","title":"Geoman: Multi-level attention networks for geo-sensory time series prediction","authors":"Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/476"},{"position":20,"raw":"[Liang et al., 2021] 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 ECML-PKDD, 2021.","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":"2021","venue":"ECML-PKDD","doi":null,"url":null,"internal_url":null},{"position":21,"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":22,"raw":"[Liang et al., 2023] Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann. Airformer: Predicting nationwide air quality in china with transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 14329-14337, 2023.","title":"Airformer: Predicting nationwide air quality in china with transformers","authors":"Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, Roger Zimmermann","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Merris, 1994] Russell Merris. Laplacian matrices of graphs: a survey. Linear algebra and its applications, 197:143- 176, 1994.","title":"Laplacian matrices of graphs: a survey","authors":"Russell Merris","year":"1994","venue":"Linear algebra and its applications","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Oord et al., 2016] Aaron van den Oord, Sander Diele- man, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw au- dio. arXiv preprint arXiv:1609.03499, 2016.","title":"Wavenet: A generative model for raw audio","authors":"Oord","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Roman et al., 2018] Cristian Roman, Ruizhi Liao, Peter Ball, Shumao Ou, and Martin de Heaver. Detecting on- street parking spaces in smart cities: Performance evalu- ation of fixed and mobile sensing systems. IEEE trans- actions on intelligent transportation systems, 19(7):2234- 2245, 2018.","title":"Detecting onstreet parking spaces in smart cities: Performance evaluation of fixed and mobile sensing systems","authors":"Cristian Roman, Ruizhi Liao, Peter Ball, Shumao Ou, Martin De Heaver","year":"2018","venue":"IEEE transactions on intelligent transportation systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Rong et al., 2018a] Yuecheng Rong, Zhimian Xu, Ruibo Yan, and Xu Ma. Du-parking: Spatio-temporal big data tells you realtime parking availability. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 646-654, 2018.","title":"Du-parking: Spatio-temporal big data tells you realtime parking availability","authors":"Rong","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Rong et al., 2018b] Yuecheng Rong, Zhimian Xu, Ruibo Yan, and Xu Ma. Du-parking: Spatio-temporal big data tells you realtime parking availability. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 646-654, 2018.","title":"Du-parking: Spatio-temporal big data tells you realtime parking availability","authors":"Rong","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Rzeszewski, 2023] Michał Rzeszewski. Mapbox. In Eval- uating Participatory Mapping Software, pages 21-40. Springer, 2023.","title":"Mapbox. In Evaluating Participatory Mapping Software","authors":"Michał Rzeszewski","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Shamsuzzoha et al., 2021] Ahm Shamsuzzoha, Juha Niem- inen, Sujan Piya, and Kendall Rutledge. Smart city for sustainable environment: A comparison of participatory strategies from helsinki, singapore and london. Cities, 114:103194, 2021.","title":"Smart city for sustainable environment: A comparison of participatory strategies from helsinki, singapore and london","authors":"Shamsuzzoha","year":"2021","venue":"Cities","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tilahun et al., 2017] Surafel Luleseged Tilahun, Giovanna Di Marzo Serugendo, et al. Cooperative multiagent sys- tem for parking availability prediction based on time vary- ing dynamic markov chains. Journal of Advanced Trans- portation, 2017, 2017.","title":"Cooperative multiagent system for parking availability prediction based on time varying dynamic markov chains","authors":"Tilahun","year":"2017","venue":"Journal of Advanced Transportation","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Toda, 1991] Hiroyuki Toda. Vector autoregression and causality. Yale University, 1991.","title":"Vector autoregression and causality","authors":"Hiroyuki Toda","year":"1991","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing sys- tems, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2007] Liqiu Wang, Xuesheng Zhou, and Xiao- hao Wei. Heat conduction: mathematical models and an- alytical solutions. Springer Science & Business Media, 2007.","title":"Heat conduction: mathematical models and analytical solutions","authors":"Wang","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2021] Sheng Wang, Zhifeng Bao, J Shane Culpepper, and Gao Cong. A survey on trajectory data management, analytics, and learning. ACM Computing Surveys (CSUR), 54(2):1-36, 2021.","title":"A survey on trajectory data management, analytics, and learning","authors":"Sheng Wang, Zhifeng Bao, Shane Culpepper, Gao Cong","year":"2021","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wu et al., 2019] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. In IJCAI, pages 1907- 1913, 7 2019.","title":"Graph wavenet for deep spatial-temporal graph modeling","authors":"Wu","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/264"},{"position":36,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. Con- necting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discov- ery & Data Mining, pages 753-763, 2020.","title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","authors":"Wu","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xia et al., 2024] Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, and Roger Zimmer- mann. Deciphering spatio-temporal graph forecasting: A causal lens and treatment. Advances in Neural Information Processing Systems, 36, 2024.","title":"Deciphering spatio-temporal graph forecasting: A causal lens and treatment","authors":"Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xie et al., 2022] Hongbin Xie, Xuan Song, and Haoran Zhang. Maas and iot: Concepts, methodologies, and ap- plications. Big Data and Mobility as a Service, pages 229- 243, 2022.","title":"Maas and iot: Concepts, methodologies, and applications","authors":"Hongbin Xie, Xuan Song, Haoran Zhang","year":"2022","venue":"Big Data and Mobility as a Service","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xu et al., 2020] Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, and Hongkai Xiong. Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908, 2020.","title":"Spatial-temporal transformer networks for traffic flow forecasting","authors":"Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xu et al., 2023] Minrui Xu, Jiajie Xu, Rui Zhou, Jianxin Li, Kai Zheng, Pengpeng Zhao, and Chengfei Liu. Empow- ering a* algorithm with neuralized variational heuristics for fastest route recommendation. IEEE Transactions on Knowledge and Data Engineering, 2023.","title":"Empowering a* algorithm with neuralized variational heuristics for fastest route recommendation","authors":"Minrui Xu, Jiajie Xu, Rui Zhou, Jianxin Li, Kai Zheng, Pengpeng Zhao, Chengfei Liu","year":"2023","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu et al., 2018] Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In IJCAI, 2018.","title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","authors":"Yu","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/505"},{"position":42,"raw":"[Zhang et al., 2017] Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In AAAI, pages 1655-1661, 2017.","title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","authors":"Junbo Zhang, Yu Zheng, Dekang Qi","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2020a] Feng Zhang, Ningxuan Feng, Yani Liu, Cheng Yang, Jidong Zhai, Shuhao Zhang, Bingsheng He, Jiazao Lin, and Xiaoyong Du. Pewlstm: Periodic lstm with weather-aware gating mechanism for parking behav- ior prediction. In IJCAI, pages 4424-4430, 2020.","title":"Pewlstm: Periodic lstm with weather-aware gating mechanism for parking behavior prediction","authors":"Zhang","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/610"},{"position":44,"raw":"[Zhang et al., 2020b] Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, and Hui Xiong. Semi-supervised hierarchi- cal recurrent graph neural network for city-wide parking availability prediction. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 34, pages 1186- 1193, 2020.","title":"Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction","authors":"Zhang","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2022] Dalin Zhang, Kaixuan Chen, Yan Zhao, Bin Yang, Lina Yao, and Christian S Jensen. Design au- tomation for fast, lightweight, and effective deep learn- ing models: A survey. arXiv preprint arXiv:2208.10498, 2022.","title":"Design automation for fast, lightweight, and effective deep learning models: A survey","authors":"Dalin Zhang, Kaixuan Chen, Yan Zhao, Bin Yang, Lina Yao, Christian S Jensen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zheng et al., 2020] Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. Gman: A graph multi- attention network for traffic prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 1234-1241, 2020.","title":"Gman: A graph multiattention network for traffic prediction","authors":"Zheng","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}