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Cited by 231Traffic Prediction and Management TechniquesTransportation Planning and OptimizationTraffic control and management

Spatiotemporal traffic forecasting: review and proposed directions

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Ermagun, Alireza, Levinson, David·2018·Transport Reviews
DOI 10.1080/01441647.2018.1442887
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Abstract

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

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  • ML offers new methods for spatio-temporal road traffic forecasting that do not always outperform other statistical methods but may transfer well between areas.

    “and it offers new methods for spatio-temporal road traffic forecasting-which do not always outperform other statistical methods [223] but may transfer well between areas [776].”

    From Tackling climate change with machine learning (2023) · cites this paper as [223]

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  1. Andrew Booth, Anthea Sutton, and Diana Papaioannou. Systematic approaches to a suc- cessful literature review. Sage, 2016.
  2. Eunjeong Ko, Jinyoung Ahn, and Eun Yi Kim. 3d markov process for traffic flow prediction in real-time. Sensors, 16(2):147, 2016.
  3. Yanyan Xu, Hui Chen, Qing-Jie Kong, Xi Zhai, and Yuncai Liu. Urban traffic flow predic- tion: a spatio-temporal variable selection-based approach. Journal of Advanced Transporta- tion, 2015.
  4. Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Trans- portation Systems, 16(2):865-873, 2015.
  5. Florin Schimbinschi, Xuan Vinh Nguyen, James Bailey, Chris Leckie, Hai Vu, and Rao Kotagiri. Traffic forecasting in complex urban networks: Leveraging big data and machine learning. In Big Data (Big Data), 2015 IEEE International Conference on, pages 1019- 1024. IEEE, 2015.
  6. Anton Agafonov and Vladislav Myasnikov. Traffic flow forecasting algorithm based on combination of adaptive elementary predictors. In International Conference on Analysis of Images, Social Networks and Texts, pages 163-174. Springer, 2015.
  7. Gaetano Fusco, Chiara Colombaroni, Luciano Comelli, and Natalia Isaenko. Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models. In Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on, pages 93- 101. IEEE, 2015.
  8. Yajie Zou, Xuedong Hua, Yanru Zhang, and Yinhai Wang. Hybrid short-term freeway speed prediction methods based on periodic analysis. Canadian Journal of Civil Engineering, 42(8):570-582, 2015.
  9. Yi Hou, Praveen Edara, and Carlos Sun. Traffic flow forecasting for urban work zones. IEEE Transactions on Intelligent Transportation Systems, 16(4):1761-1770, 2015.
  10. Xingxing Xing, Xiabing Zhou, Haikun Hong, Wenhao Huang, Kaigui Bian, and Kunqing Xie. Traffic flow decomposition and prediction based on robust principal component anal- ysis. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 2219-2224. IEEE, 2015.
  11. Jin Young Ahn, Eunjeong Ko, and EunYi Kim. Predicting spatiotemporal traffic flow based on support vector regression and bayesian classifier. In Big Data and Cloud Computing (BDCloud), 2015 IEEE Fifth International Conference on, pages 125-130. IEEE, 2015.
  12. Pietro Dell'Acqua, Francesco Bellotti, Riccardo Berta, and Alessandro De Gloria. Time- aware multivariate nearest neighbor regression methods for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 16(6):3393-3402, 2015.
  13. Jing-ting Zhong and Shuai Ling. Key factors of k-nearest neighbours nonparametric regres- sion in short-time traffic flow forecasting. In Proceedings of the 21st International Confer- ence on Industrial Engineering and Engineering Management 2014, pages 9-12. Springer, 2015.
  14. Xing-Yu Chen, Hsing-Kuo Pao, and Yuh-Jye Lee. Efficient traffic speed forecasting based on massive heterogenous historical data. In Big Data (Big Data), 2014 IEEE International Conference on, pages 10-17. IEEE, 2014.
  15. Xiaoguang Niu, Ying Zhu, and Xining Zhang. Deepsense: a novel learning mechanism for traffic prediction with taxi gps traces. In 2014 IEEE Global Communications Conference, pages 2745-2750. IEEE, 2014.
  16. Dhanya Menoth Mohan, Muhammad Tayyab Asif, Nikola Mitrovic, Justin Dauwels, and Patrick Jaillet. Wavelets on graphs with application to transportation networks. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 1707- 1712. IEEE, 2014.
  17. Jianjiang Yang, Lee Han, Phillip Freeze, Shih-Miao Chin, and Ho-Ling Hwang. Short-term freeway speed profiling based on longitudinal spatiotemporal dynamics. Transportation Research Record: Journal of the Transportation Research Board, (2467):62-72, 2014.
  18. Nedal T Ratrout. Short-term traffic flow prediction using group method data han- dling (gmdh)-based abductive networks. Arabian Journal for Science and Engineering, 39(2):631-646, 2014.
  19. Ningyu Zhao, Zhiheng Li, and Yuebiao Li. Improving the traffic data imputation accu- racy using temporal and spatial information. In Intelligent Computation Technology and Automation (ICICTA), 2014 7th International Conference on, pages 312-317. IEEE, 2014.
  20. V Fabrizi and R Ragona. A pattern matching approach to speed forecasting of traffic net- works. European Transport Research Review, 6(3):333-342, 2014.
  21. James Haworth. Spatio-temporal forecasting of network data. PhD thesis, UCL (University College London), 2014.
  22. TL Pan, Agachai Sumalee, Ren-Xin Zhong, and Nakorn Indra-Payoong. Short-term traffic state prediction based on temporal-spatial correlation. IEEE Transactions on Intelligent Transportation Systems, 14(3):1242-1254, 2013.
  23. Yufei Han and Fabien Moutarde. Statistical traffic state analysis in large-scale transporta- tion networks using locality-preserving non-negative matrix factorisation. IET Intelligent Transport Systems, 7(3):283-295, 2013.
  24. Xiaodan Huang and Wei Wang. Traffic prediction based on correlation of road sections. Indonesian Journal of Electrical Engineering and Computer Science, 11(10):5523-5529, 2013.
  25. Yiannis Kamarianakis, Wei Shen, and Laura Wynter. Real-time road traffic forecasting using regime-switching space-time models and adaptive lasso. Applied stochastic models in business and industry, 28(4):297-315, 2012.
  26. James Haworth and Tao Cheng. Non-parametric regression for space-time forecasting under missing data. Computers, Environment and Urban Systems, 36(6):538-550, 2012.
  27. Tao Cheng, James Haworth, and Jiaqiu Wang. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(4):389-413, 2012.
  28. F Guo, R Krishnan, and JW Polak. Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method. In Road Transport Information and Control (RTIC 2012), IET and ITS Conference on, pages 1-6. IET, 2012.
  29. Tianlu Pan. The stochastic dynamic journey time reliability analysis by considering the spatial and temporal correlation. PhD thesis, The Hong Kong Polytechnic University, 2012.
  30. Henry YT Ngan, Nelson HC Yung, and Anthony GO Yeh. Modeling of traffic data character- istics by dirichlet process mixtures. In 2012 IEEE International Conference on Automation Science and Engineering (CASE), pages 224-229. IEEE, 2012.
  31. Zheng-Wu Yuan and Yuan-Hui Wang. Research on k nearest neighbor non-parametric re- gression algorithm based on kd-tree and clustering analysis. In Computational and Infor- mation Sciences (ICCIS), 2012 Fourth International Conference on, pages 298-301. IEEE, 2012.
  32. Shiliang Sun, Rongqing Huang, and Ya Gao. Network-scale traffic modeling and fore- casting with graphical lasso and neural networks. Journal of Transportation Engineering, 138(11):1358-1367, 2012.
  33. A Pascale and M Nicoli. Adaptive bayesian network for traffic flow prediction. In 2011 IEEE Statistical Signal Processing Workshop (SSP), pages 177-180. IEEE, 2011.
  34. Yufei Han and Fabien Moutarde. Analysis of network-level traffic states using locality preservative non-negative matrix factorization. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 501-506. IEEE, 2011.
  35. Samitha Samaranayake, Sébastien Blandin, and Alexandre Bayen. Learning the depen- dency structure of highway networks for traffic forecast. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, pages 5983-5988. IEEE, 2011.
  36. T Cheng, J Wang, J Haworth, BJ Heydecker, and AHF Chow. Modelling dynamic space- time autocorrelations of urban transport network. In Proceedings of the 11th International Conference on Geocomputation 2011, pages 215-210, 2011.
  37. Rui Deng and Lizhu Jiang. Traffic state forecast of road network based on spatial-temporal data mining. In Third International Conference on Transportation Engineering (ICTE), 2011.
  38. Wanli Min and Laura Wynter. Real-time road traffic prediction with spatio-temporal corre- lations. Transportation Research Part C: Emerging Technologies, 19(4):606-616, 2011.
  39. Abbas Khosravi, Ehsan Mazloumi, Saeid Nahavandi, Doug Creighton, and JWC Van Lint. Prediction intervals to account for uncertainties in travel time prediction. IEEE Transactions on Intelligent Transportation Systems, 12(2):537-547, 2011.
  40. Marco Lippi, Matteo Bertini, and Paolo Frasconi. Collective traffic forecasting. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 259-273. Springer, 2010.
  41. Xinyu Min, Jianming Hu, and Zuo Zhang. Urban traffic network modeling and short-term traffic flow forecasting based on gstarima model. In Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, pages 1535-1540. IEEE, 2010.
  42. Ryan Herring, Aude Hofleitner, Pieter Abbeel, and Alexandre Bayen. Estimating arterial traffic conditions using sparse probe data. In Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, pages 929-936. IEEE, 2010.
  43. XiaoLiang Sun, LiMin Jia, HongHui Dong, Yong Qin, and Min Guo. Urban expressway traffic state forecasting based on multimode maximum entropy model. Science China Tech- nological Sciences, 53(10):2808-2816, 2010.
  44. Fangce Guo, John W Polak, and Rajesh Krishnan. Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions. In Intelligent Trans- portation Systems (ITSC), 2010 13th International IEEE Conference on, pages 1209-1214. IEEE, 2010.
  45. Ming-Tsung Lee. Short-term Freeway Traffic Flow Forecasting with ARIMAX Modeling. PhD thesis, 2010.
  46. Jennifer McCrea and Salissou Moutari. A hybrid macroscopic-based model for traffic flow in road networks. European Journal of Operational Research, 207(2):676-684, 2010.
  47. Xinyu Min, Jianming Hu, Qi Chen, Tongshuai Zhang, and Yi Zhang. Short-term traffic flow forecasting of urban network based on dynamic starima model. In 2009 12th International IEEE Conference on Intelligent Transportation Systems, pages 1-6. IEEE, 2009.
  48. Chunjiao Dong, Chunfu Shao, and Xia Li. Short-term traffic flow forecasting of road net- work based on spatial-temporal characteristics of traffic flow. In Computer Science and Information Engineering, 2009 WRI World Congress on, volume 5, pages 645-650. IEEE, 2009.
  49. Ruimin Li and Huapu Lu. Combined neural network approach for short-term urban freeway traffic flow prediction. In International Symposium on Neural Networks, pages 1017-1025. Springer, 2009.
  50. SRINIVASA RAVI CHANDRA. Spatio-temporal Analyses For Prediction Of Traffic Flow, Speed And Occupancy On I-4. PhD thesis, University of Central Florida Orlando, Florida, 2009.
  51. Bidisha Ghosh, Biswajit Basu, and Margaret O'Mahony. Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems, 10(2):246-254, 2009.
  52. CPIJ van Hinsbergen, J van Lint, and H Van Zuylen. Bayesian training and committees of state-space neural networks for online travel time prediction. Transportation Research Record: Journal of the Transportation Research Board, (2105):118-126, 2009.
  53. S Innamaa. Self-adapting traffic flow status forecasts using clustering. IET Intelligent Trans- port Systems, 3(1):67-76, 2009.
  54. Yang Yue and Anthony Gar-On Yeh. Spatiotemporal traffic-flow dependency and short-term traffic forecasting. Environment and Planning B: Planning and Design, 35(5):762-771, 2008.
  55. Antony Stathopoulos, Loukas Dimitriou, and Theodore Tsekeris. Fuzzy modeling approach for combined forecasting of urban traffic flow. Computer-Aided Civil and Infrastructure Engineering, 23(7):521-535, 2008.
  56. Corrado De Fabritiis, Roberto Ragona, and Gaetano Valenti. Traffic estimation and predic- tion based on real time floating car data. In 2008 11th International IEEE Conference on Intelligent Transportation Systems, pages 197-203. IEEE, 2008.
  57. Tianshu Wu, Kunqing Xie, Guojie Song, and Cheng Hu. A multiple svr approach with time lags for traffic flow prediction. In 2008 11th International IEEE Conference on Intelligent Transportation Systems, pages 228-233. IEEE, 2008.
  58. Cheng Hu, Kunqing Xie, Guojie Song, and Tianshu Wu. Hybrid process neural network based on spatio-temporal similarities for short-term traffic flow prediction. In 2008 11th In- ternational IEEE Conference on Intelligent Transportation Systems, pages 253-258. IEEE, 2008.
  59. JWC Van Lint. Online learning solutions for freeway travel time prediction. IEEE Transac- tions on Intelligent Transportation Systems, 9(1):38-47, 2008.
  60. Shengqi Ye, Yingjia He, Jianming Hu, and Zuo Zhang. Short-term traffic flow forecasting based on mars. In Fuzzy Systems and Knowledge Discovery, 2008. FSKD'08. Fifth Interna- tional Conference on, volume 5, pages 669-675. IEEE, 2008.
  61. Yang Yue, Anthony GO Yeh, and Yan Zhuang. Prediction time horizon and effectiveness of real-time data on short-term traffic predictability. In 2007 IEEE Intelligent Transportation Systems Conference, pages 962-967. IEEE, 2007.
  62. Shiliang Sun and Changshui Zhang. The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on intelligent transportation systems, 8(2):367-373, 2007.
  63. Yuanchang Xie and Yunlong Zhang. A wavelet network model for short-term traffic volume forecasting. Journal of Intelligent Transportation Systems, 10(3):141-150, 2006.
  64. JW Van Lint. Reliable real-time framework for short-term freeway travel time prediction. Journal of transportation engineering, 132(12):921-932, 2006.
  65. Yibing Wang, Markos Papageorgiou, and Albert Messmer. Renaissance-a unified macro- scopic model-based approach to real-time freeway network traffic surveillance. Transporta- tion Research Part C: Emerging Technologies, 14(3):190-212, 2006.
  66. Shiliang Sun, Changshui Zhang, and Yi Zhang. Traffic flow forecasting using a spatio- temporal bayesian network predictor. In International Conference on Artificial Neural Net- works, pages 273-278. Springer, 2005.
  67. Sul I Bajwa, Edward Chung, and Masao Kuwahara. Performance evaluation of an adaptive travel time prediction model. In Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005., pages 1000-1005. IEEE, 2005.
  68. Satu Innamaa. Short-term prediction of travel time using neural networks on an interurban highway. Transportation, 32(6):649-669, 2005.
  69. Sherif Ishak and Ciprian Alecsandru. Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. Journal of Trans- portation Engineering, 130(4):452-465, 2004.
  70. Yiannis Kamarianakis, Poulicos Prastacos, and D Kotzinos. Bivariate traffic relations: A space-time modeling approach. AGILE proceedings, pages 465-474, 2004.
  71. Ciprian-Danut Alecsandru. A hybrid model-based and memory-based short-term traffic prediction system. PhD thesis, Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering in The Department of Civil and Environmental Engineering By Ciprian-Danut Alecsandru B. Engr., University" Politehnica" of Bucharest, 2003.
  72. EI Vlahogianni, MG Karlaftis, and JC Golias. A multivariate neural network predictor for short term traffic forecasting in urban signalized arterial. In 10th IFAC Symposium on Control in Transportation Systems, pages 4-6, 2003.
  73. Sherif Ishak, Prashanth Kotha, and Ciprian Alecsandru. Optimization of dynamic neural network performance for short-term traffic prediction. Transportation Research Record: Journal of the Transportation Research Board, (1836):45-56, 2003.
  74. Jianming Hu, Jingyan Song, Guoqiang Yu, and Yi Zhang. A novel networked traffic param- eter forecasting method based on markov chain model. In Systems, Man and Cybernetics, 2003. IEEE International Conference on, volume 4, pages 3595-3600. IEEE, 2003.
  75. Claudia Tebaldi, Mike West, and Alan F Karr. Statistical analyses of freeway traffic flows. Journal of Forecasting, 21(1):39-68, 2002.
  76. Rik Van Grol, Domenico Inaudi, and Eric Kroes. On-line traffic condition forecasting us- ing on-line measurements and a historical database. In 7th World Congress on Intelligent Transport Systems, pages 6-9, 2000.
  77. Stepen D Clark, Mark S Dougherty, and Howard R Kirby. The use of neural networks and time series models for short term traffic forecasting: a comparative study. In TRANS- PORTATION PLANNING METHODS. PROCEEDINGS OF SEMINAR D HELD AT THE PTRC EUROPEAN TRANSPORT, HIGHWAYS AND PLANNING 21ST SUMMER ANNUAL MEETING (SEPTEMBER 13-17, 1993), UMIST. VOLUME P363, 1993.

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Authors
Ermagun, A., & Levinson, D.
Affiliations
  • Northwestern University (US)
  • The University of Sydney (AU)
Corresponding author
Ermagun, Alireza
ORCID
Venue
Transport Reviews
Publisher
Informa UK Limited
Published
2018-03-06
Volume
38
Issue
6
Pages
786-814
ISSN
  • 0144-1647 (ISSN-L)
  • 1464-5327
DOI
10.1080/01441647.2018.1442887
OpenAlex
W2521007984
Cited by
231
References
42
Citations by year
2026: 23 · 2025: 22 · 2024: 22 · 2023: 29 · 2022: 41 · 2021: 34 · 2020: 27 · 2019: 22 · 2018: 6 · 2017: 5
FWCI
17.83
Type
journal article
Language
en
Open access
Locations
  • Transport Reviews (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Minnesota Digital Conservancy (University of Minnesota) (submittedVersion)
  • Transportation Research Board 96th Annual MeetingTransportation Research Board
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Keywords
short-term traffic forecasting, traffic congestion, forecast horizon

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