{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:01:45Z","timestamp":1788361305098,"version":"build-2803163510"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p>Traffic speed histograms show the distribution of traffic speeds over a certain period. Traffic speed might not be recorded continuously, leading to missing histograms for some links on a road network. However, accurate imputation of missing histograms is a critical yet challenging task. This paper introduces a novel framework to address four previously unexplored dimensions crucial for precise traffic speed histogram estimation: regionality, proximity, sparsity, and volatility. First, to address the challenge of regionality and proximity, we employ a global partition graph that captures both regional and proximal correlations within the road network. Next, in response to the challenge of sparsity, the framework features a disentangled feature encoding pipeline, comprising a global encoder and a localized spatio-temporal encoder. This design allows for the effective handling of entangled spatio-temporal dimensions, thereby mitigating the issues related to input sparsity. In particular, the framework leverages graph neural networks and recurrent neural networks to capture spatial and temporal correlations. In addition, to encompass the complexities of spatio-temporal correlations both on global and local scales, we employ a two-layer fusion module with an attention-based mechanism for representation integration. Lastly, to mitigate the challenge of volatility due to missing values, we incorporate a self-supervised learning task using an auto-encoder framework, enhancing the stability and robustness of the encoding models. Extensive evaluations on two real-world datasets confirm that our method significantly outperforms state-of-the-art solutions in terms of both accuracy and robustness.<\/jats:p>","DOI":"10.14778\/3654621.3654628","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T18:21:08Z","timestamp":1717093268000},"page":"1605-1617","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Nuhuo: An Effective Estimation Model for Traffic Speed Histogram Imputation on A Road Network"],"prefix":"10.14778","volume":"17","author":[{"given":"Haitao","family":"Yuan","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gao","family":"Cong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2021. GAIA. https:\/\/outreach.didichuxing.com\/research\/opendata\/."},{"key":"e_1_2_1_2_1","unstructured":"2021. OpenStreetMap. https:\/\/www.openstreetmap.org."},{"key":"e_1_2_1_3_1","volume-title":"Characterizing driving styles with deep learning. CoRR","author":"Dong Weishan","year":"2016","unstructured":"Weishan Dong, Jian Li, Renjie Yao, Changsheng Li, Ting Yuan, and Lanjun Wang. 2016. Characterizing driving styles with deep learning. CoRR (2016)."},{"key":"e_1_2_1_4_1","volume-title":"Gstnet: Global spatial-temporal network for traffic flow prediction. In IJCAI. 10--16.","author":"Fang Shen","year":"2019","unstructured":"Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2019. Gstnet: Global spatial-temporal network for traffic flow prediction. In IJCAI. 10--16."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Qiang Gao Fan Zhou Kunpeng Zhang Goce Trajcevski Xucheng Luo and Fengli Zhang. 2017. Identifying Human Mobility via Trajectory Embeddings.. In IJCAI. 1689--1695.","DOI":"10.24963\/ijcai.2017\/234"},{"key":"e_1_2_1_6_1","volume-title":"Jensen","author":"Guo Chenjuan","year":"2018","unstructured":"Chenjuan Guo, Bin Yang, Jilin Hu, and Christian S. Jensen. 2018. Learning to Route with Sparse Trajectory Sets. In ICDE. 1073--1084."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_2_1_8_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. 770--778."},{"key":"e_1_2_1_9_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_2_1_10_1","volume-title":"Jensen","author":"Hu Jilin","year":"2019","unstructured":"Jilin Hu, Chenjuan Guo, Bin Yang, and Christian S. Jensen. 2019. Stochastic Weight Completion for Road Networks Using Graph Convolutional Networks. In ICDE. 1274--1285."},{"key":"e_1_2_1_11_1","volume-title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting. In ICDE.","author":"Jiang Yue","year":"2024","unstructured":"Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F. Lentzakis, and Gao Cong. 2024. SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting. In ICDE."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1006\/jpdc.1997.1403"},{"key":"e_1_2_1_13_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR(Poster)."},{"key":"e_1_2_1_14_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster)."},{"key":"e_1_2_1_15_1","volume-title":"Austin Reiter, and Gregory D Hager","author":"Lea Colin","year":"2017","unstructured":"Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. 2017. Temporal convolutional networks for action segmentation and detection. In CVPR. 156--165."},{"key":"e_1_2_1_16_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. ICLR(Poster)","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. ICLR(Poster) (2018)."},{"key":"e_1_2_1_17_1","volume-title":"ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning. In WWW. 3056--3066.","author":"Li Yanan","year":"2023","unstructured":"Yanan Li, Haitao Yuan, Zhe Fu, Xiao Ma, Mengwei Xu, and Shangguang Wang. 2023. ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning. In WWW. 3056--3066."},{"key":"e_1_2_1_18_1","volume-title":"PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation","author":"Liu Mingzhe","year":"1927","unstructured":"Mingzhe Liu, Han Huang, Hao Feng, Leilei Sun, Bowen Du, and Yanjie Fu. 2023. PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation. In IEEE. 1927--1939."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8306.2004.09402005.x"},{"key":"e_1_2_1_20_1","first-page":"1","article-title":"Spatio-temporal graph convolutional network for stochastic traic speed imputation","volume":"14","author":"Mu\u00f1iz-Cuza Carlos Enrique","year":"2022","unstructured":"Carlos Enrique Mu\u00f1iz-Cuza, Nguyen Ho, Eleni Tzirita Zacharatou, Torben Bach Pedersen, and Bin Yang. 2022. Spatio-temporal graph convolutional network for stochastic traic speed imputation. In SIGSPATIAL. 14:1--14:12.","journal-title":"SIGSPATIAL."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Bei Pan Yu Zheng David Wilkie and Cyrus Shahabi. 2013. Crowd sensing of traic anomalies based on human mobility and social media. In SIGSPATIAL. 344--353.","DOI":"10.1145\/2525314.2525343"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In SIGKDD. 1720--1730.","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397248"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3617500"},{"key":"e_1_2_1_25_1","unstructured":"Xuan Song Hiroshi Kanasugi and Ryosuke Shibasaki. 2016. DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level.. In IJCAI. 2618--2624."},{"key":"e_1_2_1_26_1","volume-title":"Visualizing data using t-SNE. JMLR 9, 11","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR 9, 11 (2008)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342265"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.08.104"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Hao Wu Ziyang Chen Weiwei Sun Baihua Zheng and Wei Wang. 2017. Modeling Trajectories with Recurrent Neural Networks. In IJCAI. 3083--3090.","DOI":"10.24963\/ijcai.2017\/430"},{"key":"e_1_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Qianxiong Xu Sijie Ruan Cheng Long Liang Yu and Chen Zhang. 2022. Traffic Speed Imputation with Spatio-Temporal Attentions and Cycle-Perceptual Training. In CIKM. 2280--2289.","DOI":"10.1145\/3511808.3557480"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8090414"},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Haitao Yuan and Guoliang Li. 2019. Distributed In-memory Trajectory Similarity Search and Join on Road Network. In ICDE. 1262--1273.","DOI":"10.1109\/ICDE.2019.00115"},{"key":"e_1_2_1_33_1","first-page":"63","article-title":"A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Sci","volume":"6","author":"Yuan Haitao","year":"2021","unstructured":"Haitao Yuan and Guoliang Li. 2021. A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Sci. Eng. 6, 1 (2021), 63--85.","journal-title":"Eng."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3570690.3570691"},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Haitao Yuan Guoliang Li Zhifeng Bao and Ling Feng. 2020. Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. In SIGMOD. 2135--2149.","DOI":"10.1145\/3318464.3389771"},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Haitao Yuan Guoliang Li Zhifeng Bao and Ling Feng. 2021. An Effective Joint Prediction Model for Travel Demands and Traic Flows. In ICDE.","DOI":"10.1109\/ICDE51399.2021.00037"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Junbo Zhang Yu Zheng Dekang Qi Ruiyuan Li and Xiuwen Yi. 2016. DNN-based prediction model for spatio-temporal data. In SIGSPATIAL. 1--4.","DOI":"10.1145\/2996913.2997016"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3654621.3654628","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T14:57:46Z","timestamp":1732114666000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3654621.3654628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.14778\/3654621.3654628"],"URL":"https:\/\/doi.org\/10.14778\/3654621.3654628","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"2024-05-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}