Most cited papers in Traffic Prediction and Management Techniques
What are the most cited papers in Traffic Prediction and Management Techniques?
The most cited paper in Traffic Prediction and Management Techniques is "Spurious regressions in econometrics" (Clive W. J. Granger, Paul Newbold, 1974), with 6,269 citations. Next come "Machine Learning: Algorithms, Real-World Applications and Research Directions" (5,413) and "On kinematic waves II. A theory of traffic flow on long crowded roads" (4,671).
Showing 50 of 100 papers. Every paper with more than 670 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Spurious regressions in econometrics Clive W. J. Granger, Paul Newbold · Journal of Econometrics | Jul 1974 | 6,269 |
| 2 | Machine Learning: Algorithms, Real-World Applications and Research Directions Iqbal H. Sarker · SN Computer Science | 22 Mar 2021 | 5,413 |
| 3 | On kinematic waves II. A theory of traffic flow on long crowded roads Michael James Lighthill, Gerald Beresford Whitham · Proceedings of the Royal Society of London A Mathematical and Physical Sciences | 10 May 1955 | 4,671 |
| 4 | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting Bing Yu, Haoteng Yin, Zhanxing Zhu | Jul 2018 | 3,631 |
| 5 | T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction Ling Zhao, Yujiao Song, Chao Zhang et al. · IEEE Transactions on Intelligent Transportation Systems | 22 Aug 2019 | 3,346 |
| 6 | Dynamical model of traffic congestion and numerical simulation Masako Bando, Katsuya Hasebe, A. Nakayama et al. · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics | Feb 1995 | 3,052 |
| 7 | Traffic Flow Prediction With Big Data: A Deep Learning Approach Yisheng Lv, Yanjie Duan, Wenwen Kang et al. · IEEE Transactions on Intelligent Transportation Systems | 2014 | 3,012 |
| 8 | Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting Shengnan Guo, Youfang Lin, Ning Feng et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 17 Jul 2019 | 2,973 |
| 9 | Graph WaveNet for Deep Spatial-Temporal Graph Modeling Zonghan Wu, Shirui Pan, Guodong Long et al. | 28 Jul 2019 | 2,951 |
| 10 | The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory Carlos F. Daganzo · Transportation Research Part B Methodological | Aug 1994 | 2,951 |
| 11 | Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network Weicong Kong, Zhao Yang Dong, Youwei Jia et al. · IEEE Transactions on Smart Grid | 18 Sep 2017 | 2,652 |
| 12 | Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction Junbo Zhang, Yu Zheng, Dekang Qi · Proceedings of the AAAI Conference on Artificial Intelligence | 12 Feb 2017 | 2,241 |
| 13 | Long short-term memory neural network for traffic speed prediction using remote microwave sensor data Xiaolei Ma, Zhimin Tao, Yinhai Wang et al. · Transportation Research Part C Emerging Technologies | 7 Apr 2015 | 2,221 |
| 14 | Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results Billy M. Williams, L A Hoel · Journal of Transportation Engineering | 20 Oct 2003 | 2,175 |
| 15 | Recent Development and Applications of SUMO - Simulation of Urban MObility Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch et al. · elib (German Aerospace Center) | 31 Dec 2012 | 1,977 |
| 16 | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks Zonghan Wu, Shirui Pan, Guodong Long et al. | 20 Aug 2020 | 1,970 |
| 17 | LSTM network: a deep learning approach for short‐term traffic forecast Zheng Zhao, Weihai Chen, Xingming Wu et al. · IET Intelligent Transport Systems | 12 Jan 2017 | 1,846 |
| 18 | Data-Driven Intelligent Transportation Systems: A Survey Junping Zhang, Fei–Yue Wang, Kunfeng Wang et al. · IEEE Transactions on Intelligent Transportation Systems | 26 Jul 2011 | 1,823 |
| 19 | GMAN: A Graph Multi-Attention Network for Traffic Prediction Chuanpan Zheng, Xiaoliang Fan, Cheng Wang et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 3 Apr 2020 | 1,714 |
| 20 | Trajectory Data Mining Yu Zheng · ACM Transactions on Intelligent Systems and Technology | 12 May 2015 | 1,670 |
| 21 | Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting Chao Song, Youfang Lin, Shengnan Guo et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 3 Apr 2020 | 1,590 |
| 22 | Nonlinear Follow-the-Leader Models of Traffic Flow Denos C. Gazis, Robert K Herman, Richard W. Rothery · Operations Research | Aug 1961 | 1,530 |
| 23 | The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics Bart van Arem, C.J.G. van Driel, Ruben Visser · IEEE Transactions on Intelligent Transportation Systems | Dec 2006 | 1,509 |
| 24 | Travel-Time Prediction With Support Vector Regression Chih-Hsien Wu, Jan-Ming Ho, Dong-Hyun Lee · IEEE Transactions on Intelligent Transportation Systems | Dec 2004 | 1,455 |
| 25 | Using LSTM and GRU neural network methods for traffic flow prediction Rui Fu, Zuo‐Feng Zhang, Li Li | Nov 2016 | 1,439 |
| 26 | Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction Xiaolei Ma, Zhuang Dai, Zhengbing He et al. · Sensors | 10 Apr 2017 | 1,432 |
| 27 | Urban Computing Yu Zheng, Licia Capra, Ouri Wolfson et al. · ACM Transactions on Intelligent Systems and Technology | 18 Sep 2014 | 1,370 |
| 28 | A Multiagent Approach to Autonomous Intersection Management Kurt Dresner, Peter Stone · Journal of Artificial Intelligence Research | 31 Mar 2008 | 1,330 |
| 29 | Graph neural network for traffic forecasting: A survey Weiwei Jiang, Jiayun Luo · Expert Systems with Applications | 23 Jun 2022 | 1,316 |
| 30 | Driving speed and the risk of road crashes: A review L T Aarts, Ingrid van Schagen · Accident Analysis & Prevention | 28 Oct 2005 | 1,281 |
| 31 | Review of road traffic control strategies Markos Papageorgiou, C. Kiakaki, Vaya Dinopoulou et al. · Proceedings of the IEEE | 20 Nov 2003 | 1,249 |
| 32 | Short-term traffic forecasting: Where we are and where we’re going Eleni I. Vlahogianni, Matthew G. Karlaftis, John Golias · Transportation Research Part C Emerging Technologies | 4 Feb 2014 | 1,202 |
| 33 | AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems Iqbal H. Sarker · SN Computer Science | 10 Feb 2022 | 1,176 |
| 34 | Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning Wenhao Huang, Guojie Song, Haikun Hong et al. · IEEE Transactions on Intelligent Transportation Systems | 10 Apr 2014 | 1,154 |
| 35 | Dynamic prediction of traffic volume through Kalman filtering theory Iwao Okutani, Yorgos J. Stephanedes · Transportation Research Part B Methodological | Feb 1984 | 1,106 |
| 36 | Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction Huaxiu Yao, Fei Qing Wu, Jintao Ke et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 26 Apr 2018 | 1,103 |
| 37 | Big data, smart cities and city planning Michael Batty · Dialogues in Human Geography | Nov 2013 | 1,091 |
| 38 | Big Data Analytics in Intelligent Transportation Systems: A Survey Li Zhu, Fei Richard Yu, Yige Wang et al. · IEEE Transactions on Intelligent Transportation Systems | 23 Apr 2018 | 1,090 |
| 39 | Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis Amir Bahador Parsa, Ali Movahedi, Homa Taghipour et al. · Accident Analysis & Prevention | 19 Dec 2019 | 1,069 |
| 40 | Federated learning on non-IID data: A survey Hangyu Zhu, Jinjin Xu, Shiqing Liu et al. · Neurocomputing | 6 Sep 2021 | 1,047 |
| 41 | Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control Tianshu Chu, Jie Wang, Lara Codecà et al. · IEEE Transactions on Intelligent Transportation Systems | 15 Mar 2019 | 1,045 |
| 42 | Deep learning for short-term traffic flow prediction Nicholas G. Polson, Vadim Sokolov · Transportation Research Part C Emerging Technologies | 17 Mar 2017 | 1,031 |
| 43 | Hidden Markov map matching through noise and sparseness Paul Newson, John C. Krumm | 4 Nov 2009 | 1,031 |
| 44 | Comparison of parametric and nonparametric models for traffic flow forecasting Brian Lee Smith, Billy M. Williams, Rudy Oswald · Transportation Research Part C Emerging Technologies | Aug 2002 | 1,013 |
| 45 | Tackling Climate Change with Machine Learning Lynn H. Kaack et al. · OPUS 4 (Zuse Institute Berlin) | 7 Feb 2022 | 994 |
| 46 | Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting Zhiyong Cui, Kristian C. Henrickson, Ruimin Ke et al. · IEEE Transactions on Intelligent Transportation Systems | 28 Nov 2019 | 993 |
| 47 | Urban planning and building smart cities based on the Internet of Things using Big Data analytics M. Mazhar Rathore, Awais Ahmad, Anand Paul et al. · Computer Networks | 11 Jan 2016 | 940 |
| 48 | Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment Juan Carlos Herrera, Daniel B. Work, Ryan Herring et al. · Transportation Research Part C Emerging Technologies | 29 Dec 2009 | 926 |
| 49 | Machine learning for internet of things data analysis: a survey Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain et al. · Digital Communications and Networks | 13 Oct 2017 | 914 |
| 50 | Current map-matching algorithms for transport applications: State-of-the art and future research directions Mohammed Abdul Quddus, Washington Yotto Ochieng, Robert B. Noland · Transportation Research Part C Emerging Technologies | 28 Jun 2007 | 907 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.