{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/traffic-prediction/papers/4","list_of":"/task/traffic-prediction","task":"Traffic Prediction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,375],"of":375,"counts":{"archive_papers_tagged":375,"with_a_code_link":164,"where_syntology_ran_a_sample":36,"not_listed_spam_title":0,"listed":375,"listed_where_code_ran":36,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":31,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":31,"listed_every_run_a_failure_of_syntologys_instrument":5,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/traffic-prediction","prev":"/task/traffic-prediction/papers/3","next":null,"papers":[{"url":null,"slug":"detectornet-transformer-enhanced-spatial","title":"DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction","date":"2021-10-19","arxiv_id":"2111.00869","repositories_listed":0,"syntology":null},{"url":null,"slug":"5g-traffic-prediction-with-time-series","title":"5G Traffic Prediction with Time Series Analysis","date":"2021-10-07","arxiv_id":"2110.03781","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictionnet-real-time-joint-probabilistic","title":"PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation","date":"2021-09-23","arxiv_id":"2109.11094","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-traffic-prediction-using-physics","title":"Short-term traffic prediction using physics-aware neural networks","date":"2021-09-21","arxiv_id":"2109.10253","repositories_listed":0,"syntology":null},{"url":null,"slug":"space-meets-time-local-spacetime-neural","title":"Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting","date":"2021-09-11","arxiv_id":"2109.05225","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatially-focused-attack-against","title":"Spatially Focused Attack against Spatiotemporal Graph Neural Networks","date":"2021-09-10","arxiv_id":"2109.04608","repositories_listed":0,"syntology":null},{"url":null,"slug":"linkteller-recovering-private-edges-from","title":"LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis","date":"2021-08-14","arxiv_id":"2108.06504","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-based-fast-uplink-grant-for","title":"A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory","date":"2021-08-02","arxiv_id":"2108.10070","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-method-for-analyzing-weather-effect","title":"A Novel Method for Analyzing Weather Effect on Smart City Traffic","date":"2021-07-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qfcnn-quantum-fourier-convolutional-neural","title":"QFCNN: Quantum Fourier Convolutional Neural Network","date":"2021-06-19","arxiv_id":"2106.10421","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-aware-service-relocation-in-cloud","title":"Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks","date":"2021-05-17","arxiv_id":"2105.07653","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-artificial-intelligence-1","title":"Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems","date":"2021-04-21","arxiv_id":"2105.15103","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-fold-correlation-attention-network-for","title":"Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency","date":"2021-04-19","arxiv_id":"2104.09083","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-graph-convolutional-network-for","title":"Bayesian Graph Convolutional Network for Traffic Prediction","date":"2021-04-01","arxiv_id":"2104.00488","repositories_listed":0,"syntology":null},{"url":"/paper/spatial-temporal-tensor-graph-convolutional","slug":"spatial-temporal-tensor-graph-convolutional","title":"Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction","date":"2021-03-10","arxiv_id":"2103.06126","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-driven-source-traffic-prediction-in","title":"Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks","date":"2021-01-12","arxiv_id":"2101.04365","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-spatial-nonstationarity-via","title":"Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction","date":"2021-01-08","arxiv_id":"2101.12010","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-intervals-for-graph-based-spatio","title":"Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction","date":"2020-12-09","arxiv_id":"2012.05207","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic4cast-2020-graph-ensemble-net-and-the","title":"Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction","date":"2020-12-03","arxiv_id":"2012.02115","repositories_listed":0,"syntology":null},{"url":null,"slug":"ast-gcn-attribute-augmented-spatiotemporal","title":"AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting","date":"2020-11-22","arxiv_id":"2011.11004","repositories_listed":0,"syntology":null},{"url":"/paper/bayesian-spatio-temporal-graph-convolutional","slug":"bayesian-spatio-temporal-graph-convolutional","title":"Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting","date":"2020-10-15","arxiv_id":"2010.07498","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-twitter-to-traffic-predictor-next-day","title":"From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data","date":"2020-09-29","arxiv_id":"2009.13794","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-deep-learning-in-predictive","title":"Demystifying Deep Learning in Predictive Spatio-Temporal Analytics: An Information-Theoretic Framework","date":"2020-09-14","arxiv_id":"2009.06304","repositories_listed":0,"syntology":null},{"url":null,"slug":"particle-swarm-optimized-federated-learning","title":"Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services","date":"2020-09-05","arxiv_id":"2009.02560","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-traffic-prediction-with-deep","title":"Short-term Traffic Prediction with Deep Neural Networks: A Survey","date":"2020-08-28","arxiv_id":"2009.00712","repositories_listed":0,"syntology":null},{"url":null,"slug":"igani-iterative-generative-adversarial","title":"IGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Data","date":"2020-08-11","arxiv_id":"2008.04847","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-the-modifiable-areal-unit-problem","title":"Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics","date":"2020-07-30","arxiv_id":"2007.15486","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-spatio-temporal-graph-convolutional","title":"Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data","date":"2020-06-23","arxiv_id":"2006.12715","repositories_listed":0,"syntology":null},{"url":"/paper/traffic-transformer-capturing-the-continuity","slug":"traffic-transformer-capturing-the-continuity","title":"Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting","date":"2020-06-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-performance-of-bittorrent-traffic","title":"Improved Performance of BitTorrent Traffic Prediction Using Kalman Filter","date":"2020-06-09","arxiv_id":"2006.05540","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-dynamic-graph-attention","title":"Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction","date":"2020-06-07","arxiv_id":"2006.05905","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-bidirectional-and-unidirectional-lstm","title":"Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values","date":"2020-05-24","arxiv_id":"2005.11627","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-recommend-signal-plans-under","title":"Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction","date":"2020-05-21","arxiv_id":"2005.13522","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-dynamic-spatio-temporal","title":"An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction","date":"2020-05-08","arxiv_id":"2005.05128","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-traffic-prediction","title":"Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions","date":"2020-04-18","arxiv_id":"2004.08555","repositories_listed":0,"syntology":null},{"url":"/paper/spatio-temporal-graph-structure-learning-for","slug":"spatio-temporal-graph-structure-learning-for","title":"Spatio-Temporal Graph Structure Learning for Traffic Forecasting","date":"2020-04-03","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"buildsensys-reusing-building-sensing-data-for","title":"BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning","date":"2020-03-11","arxiv_id":"2003.06309","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-predictive-deployment-of","title":"Machine Learning for Predictive Deployment of UAVs with Multiple Access","date":"2020-03-02","arxiv_id":"2003.02631","repositories_listed":0,"syntology":null},{"url":null,"slug":"dalc-distributed-automatic-lstm-customization","title":"DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction","date":"2020-01-24","arxiv_id":"2001.09821","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinear-traffic-prediction-as-a-matrix","title":"Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning","date":"2020-01-08","arxiv_id":"2001.02492","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-to-scale-up-time-series-traffic","title":"On model selection for scalable time series forecasting in transport networks","date":"2019-11-29","arxiv_id":"1911.13042","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolution-networks-for-probabilistic","title":"Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration","date":"2019-11-22","arxiv_id":"1911.09837","repositories_listed":0,"syntology":null},{"url":"/paper/vluc-an-empirical-benchmark-for-video-like","slug":"vluc-an-empirical-benchmark-for-video-like","title":"VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction","date":"2019-11-16","arxiv_id":"1911.06982","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-deep-networks-in-intelligent","title":"Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study","date":"2019-11-08","arxiv_id":"1911.03010","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-mobile-traffic-with","title":"Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression","date":"2019-07-25","arxiv_id":"1907.10865","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-flow-combination-forecasting-method","title":"Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA","date":"2019-06-25","arxiv_id":"1906.10407","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600560","title":"Revisiting Flow Information for Traffic Prediction","date":"2019-06-03","arxiv_id":"1906.00560","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600939","title":"Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA","date":"2019-06-03","arxiv_id":"1906.00939","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-wireless-demand-with-extreme","title":"Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes","date":"2019-05-15","arxiv_id":"1905.06744","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600951","title":"User Traffic Prediction for Proactive Resource Management: Learning-Powered Approaches","date":"2019-05-09","arxiv_id":"1906.00951","repositories_listed":0,"syntology":null},{"url":null,"slug":"190500702","title":"Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization","date":"2019-04-25","arxiv_id":"1905.00702","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-aware-convolutional-networks-for","title":"Position-Aware Convolutional Networks for Traffic Prediction","date":"2019-04-12","arxiv_id":"1904.06187","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-deep-graph-infomax","title":"Spatio-Temporal Deep Graph Infomax","date":"2019-04-12","arxiv_id":"1904.06316","repositories_listed":0,"syntology":null},{"url":"/paper/st-unet-a-spatio-temporal-u-network-for-graph","slug":"st-unet-a-spatio-temporal-u-network-for-graph","title":"ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling","date":"2019-03-13","arxiv_id":"1903.05631","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-modelling-traffic","title":"Graph Neural Networks for Modelling Traffic Participant Interaction","date":"2019-03-04","arxiv_id":"1903.01254","repositories_listed":0,"syntology":null},{"url":"/paper/3d-graph-convolutional-networks-with-temporal","slug":"3d-graph-convolutional-networks-with-temporal","title":"3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting","date":"2019-03-03","arxiv_id":"1903.00919","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-road-traffic-prediction-based-on","title":"Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks","date":"2019-02-25","arxiv_id":"1902.09601","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-traffic-prediction-with-scalable","title":"Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification","date":"2019-02-13","arxiv_id":"1902.04763","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-output-gaussian-processes-for","title":"Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation","date":"2018-12-20","arxiv_id":"1812.08739","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-spatio-temporal-graph-based-cnns-for","title":"Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction","date":"2018-12-05","arxiv_id":"1812.02019","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-metropolitan-traffic-prediction","title":"Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network","date":"2018-11-02","arxiv_id":"1811.00740","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-online-hyperparameter-optimization","title":"Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction","date":"2018-11-01","arxiv_id":"1811.00620","repositories_listed":0,"syntology":null},{"url":null,"slug":"travel-speed-prediction-with-a-hierarchical","title":"Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework","date":"2018-09-06","arxiv_id":"1809.01887","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fine-grained-network-flow-prediction","title":"Towards Fine Grained Network Flow Prediction","date":"2018-08-20","arxiv_id":"1808.06453","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-bounded-total-variation","title":"Application of Bounded Total Variation Denoising in Urban Traffic Analysis","date":"2018-08-04","arxiv_id":"1808.03258","repositories_listed":0,"syntology":null},{"url":null,"slug":"call-detail-records-driven-anomaly-detection","title":"Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks","date":"2018-07-30","arxiv_id":"1807.11545","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-neural-networks-for-space","title":"Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery","date":"2018-04-23","arxiv_id":"1804.08562","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatically-inferring-data-quality-for","title":"Automatically Inferring Data Quality for Spatiotemporal Forecasting","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-signal-processing-approach-for-real","title":"A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks","date":"2017-11-19","arxiv_id":"1711.06954","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-prediction-based-on-random","title":"Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory","date":"2017-11-08","arxiv_id":"1711.02833","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-recurrent-convolutional-1","title":"Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks","date":"2017-05-07","arxiv_id":"1705.02699","repositories_listed":0,"syntology":null},{"url":null,"slug":"ranking-in-evolving-complex-networks","title":"Ranking in evolving complex networks","date":"2017-04-26","arxiv_id":"1704.08027","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-learning-and-prediction-of-application","title":"The Learning and Prediction of Application-level Traffic Data in Cellular Networks","date":"2016-06-15","arxiv_id":"1606.04778","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-network-traffic-analysis-and","title":"A Review of Network Traffic Analysis and Prediction Techniques","date":"2015-07-21","arxiv_id":"1507.05722","repositories_listed":0,"syntology":null},{"url":"/paper/10000-times-accelerated-robust-subset","slug":"10000-times-accelerated-robust-subset","title":"10,000+ Times Accelerated Robust Subset Selection (ARSS)","date":"2014-09-12","arxiv_id":"1409.3660","repositories_listed":0,"syntology":null}],"record_sha256":"0c0ee77ba988b99ef062765da6114a8df747ea4fb7c726fd80de0115ae538fd9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}