{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:24:45Z","timestamp":1771525485532,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599357","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"649-660","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Frigate: Frugal Spatio-temporal Forecasting on Road Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4343-4263","authenticated-orcid":false,"given":"Mridul","family":"Gupta","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Delhi, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3166-2712","authenticated-orcid":false,"given":"Hariprasad","family":"Kodamana","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4147-9372","authenticated-orcid":false,"given":"Sayan","family":"Ranu","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Delhi, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems","author":"Bai Lei","year":"2020","unstructured":"Lei Bai , Lina Yao , Can Li , Xianzhi Wang , and Can Wang . 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems , Vol. 33 ( 2020 ), 17804--17815. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems , Vol. 33 (2020), 17804--17815."},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR, 1684--1694","author":"Chen Yuzhou","year":"2021","unstructured":"021)]% zigzag, Yuzhou Chen , Ignacio Segovia , and Yulia R Gel . 2021 . Z-GCNETs: time zigzags at graph convolutional networks for time series forecasting . In International Conference on Machine Learning. PMLR, 1684--1694 . 021)]% zigzag, Yuzhou Chen, Ignacio Segovia, and Yulia R Gel. 2021. Z-GCNETs: time zigzags at graph convolutional networks for time series forecasting. In International Conference on Machine Learning. PMLR, 1684--1694."},{"key":"e_1_3_2_2_3_1","volume-title":"Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini.","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi , Ladislav Ramp\u00e1vs ek , Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022 . Long Range Graph Benchmark. In NeurIPS. Vijay Prakash Dwivedi, Ladislav Ramp\u00e1vs ek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022. Long Range Graph Benchmark. In NeurIPS."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_2_5_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . 2017. Inductive representation learning on large graphs. Advances in neural information processing systems , Vol. 30 ( 2017 ). Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems , Vol. 30 (2017)."},{"key":"e_1_3_2_2_6_1","first-page":"22070","article-title":"NeuroMLR: Robust & Reliable Route Recommendation on Road Networks","volume":"34","author":"Jain Jayant","year":"2021","unstructured":"Jayant Jain , Vrittika Bagadia , Sahil Manchanda , and Sayan Ranu . 2021 . NeuroMLR: Robust & Reliable Route Recommendation on Road Networks . Advances in Neural Information Processing Systems , Vol. 34 (2021), 22070 -- 22082 . Jayant Jain, Vrittika Bagadia, Sahil Manchanda, and Sayan Ranu. 2021. NeuroMLR: Robust & Reliable Route Recommendation on Road Networks. Advances in Neural Information Processing Systems , Vol. 34 (2021), 22070--22082.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00207"},{"key":"e_1_3_2_2_8_1","volume-title":"DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. In International Conference on Machine Learning. PMLR, 11906--11917","author":"Lan Shiyong","year":"2022","unstructured":"Shiyong Lan , Yitong Ma , Weikang Huang , Wenwu Wang , Hongyu Yang , and Pyang Li . 2022 . DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. In International Conference on Machine Learning. PMLR, 11906--11917 . Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. 2022. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. In International Conference on Machine Learning. PMLR, 11906--11917."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3219618"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313418"},{"key":"e_1_3_2_2_12_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18)","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2018 . Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18) . Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3277868.3277870"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01200757"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539397"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/3213880.3213889"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218449"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.46"},{"key":"e_1_3_2_2_19_1","volume-title":"GraphReach: Position-Aware Graph Neural Network using Reachability Estimations. IJCAI","author":"Nishad Sunil","year":"2021","unstructured":"Sunil Nishad , Shubhangi Agarwal , Arnab Bhattacharya , and Sayan Ranu . 2021. GraphReach: Position-Aware Graph Neural Network using Reachability Estimations. IJCAI ( 2021 ). Sunil Nishad, Shubhangi Agarwal, Arnab Bhattacharya, and Sayan Ranu. 2021. GraphReach: Position-Aware Graph Neural Network using Reachability Estimations. IJCAI (2021)."},{"key":"e_1_3_2_2_20_1","unstructured":"OpenStreetMap contributors. 2017. Planet dump retrieved from https:\/\/planet.osm.org . https:\/\/www.openstreetmap.org.  OpenStreetMap contributors. 2017. Planet dump retrieved from https:\/\/planet.osm.org . https:\/\/www.openstreetmap.org."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137647"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_2_23_1","volume-title":"Graph attention networks. ICLR","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018. Graph attention networks. ICLR ( 2018 ). Petar Velivc kovi\u0107 , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR (2018)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3176653"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Yang Wang Yiwei Xiao Xike Xie Ruoyu Chen and Hengchang Liu. 2018. Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information.. In IJCAI. 3571--3577.  Yang Wang Yiwei Xiao Xike Xie Ruoyu Chen and Hengchang Liu. 2018. Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information.. In IJCAI. 3571--3577.","DOI":"10.24963\/ijcai.2018\/496"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_3_2_2_28_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019. How powerful are graph neural networks? ICLR ( 2019 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2017.1400548"},{"key":"e_1_3_2_2_30_1","volume-title":"Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 817--826","author":"Yang Song","year":"2021","unstructured":"Song Yang , Jiamou Liu , and Kaiqi Zhao . 2021 . Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 817--826 . Song Yang, Jiamou Liu, and Kaiqi Zhao. 2021. Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 817--826."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00060"},{"key":"e_1_3_2_2_32_1","volume-title":"Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You , Rex Ying , and Jure Leskovec . 2019 . Position-aware Graph Neural Networks. In ICML , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . 7134--7143. Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware Graph Neural Networks. In ICML, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. 7134--7143."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch22"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599357","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:47Z","timestamp":1750178267000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":35,"alternative-id":["10.1145\/3580305.3599357","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599357","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}