Abstract
In this paper, we propose a hybrid prediction model based on spatial-temporal data fusion to predict future tunnel traffic. Our approach consists of a local predictor, a global predictor, an outlier predictor, and a prediction integrator. Firstly, the local predictor forecast tunnel traffic based on the collected local data. It is more concerned with the historical and future traffic conditions, that is, the temporal correlation. Then, the global predictor uses data collected from peripheral road segments to predict tunnel volume, which models the spatial correlation based on a deep learning network. Thirdly, the prediction integrator dynamically integrates the prediction results of local and global predictors on the basis of the current weather conditions. In addition, we detect the abnormal traffic volume for training an individual outlier predictor. Finally, we integrate it with the output of the prediction integrator and accumulate the current tunnel traffic volume to calculate final prediction results. In our experiments, we collected the multisource urban-awareness data from Shanghai to evaluate the proposed hybrid prediction model. Our approach is obviously superior to the baseline when dealing with the general condition. The MREs of 30 min and 60 min tunnel traffic volume prediction are less than 6.5%. In addition, the outlier predictor of the proposed model significantly enhances the ability to predict the abnormal tunnel traffic under extreme weather conditions or unexpected traffic accidents.
Similar content being viewed by others
References
Williams BM, Hoel LA (2003) Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. J Transp Eng 129(6):664–672
Y. Chen and L. Wang, "Traffic Flow Prediction with Big Data: A Deep Learning based Time Series Model," in IEEE INFOCOM 2017 -Ieee Conference on Computer Communications Workshops, 2017, pp. 1010–1011
Yang F, Yin Z, Liu H, Ran B (2004) Online Recursive Algorithm for Short-Term Traffic Prediction. Trans Res Rec J Transp Res Board 1879(1):1–8
Chang H, Lee Y, Yoon B, Baek S (2012) Dynamic near-term traffic flow prediction: systemoriented approach based on past experiences. IET Intell Transp Syst 6(3):292–305
Kumar K, Parida M, Katiyar VK (2013) Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network ☆. Procedia Soc Behav Sci 104:755–764
Huang W, Song G, Hong H, Xie K (2014) Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning. IEEE Trans Intell Transp Syst 15(5):2191–2201
Yang HF, Dillon TS, Chen YP (2016) Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach. IEEE Trans Neural Netw Learn Syst 99:1–11
H. Tan, X. Xuan, Y. Wu, Z. Zhong, and B. Ran, "A Comparison of Traffic Flow Prediction Methods Based on DBN," in Cota International Conference of Transportation Professionals, 2016, pp. 273–283
Koesdwiady A, Soua R, Karray F (2016) Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach. IEEE Trans Veh Technol 65(12):9508–9517
Y. Wu and H. Tan, "Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning frameework," 2016
H. Yi, H. J. Jung, and S. Bae, "Deep Neural Networks for traffic flow prediction," in IEEE International Conference on Big Data and Smart Computing, 2017, pp. 328–331
Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17
B. D. Greenshields, "A study of traffic capacity," in Highway research board proceedings, 1935
J. Shang, Y. Zheng, W. Tong, E. Chang, and Y. Yu, "Inferring gas consumption and pollution emission of vehicles throughout a city," in Acm Sigkdd International Conference on Knowledge Discovery & Data Mining, 2014, pp. 1027–1036
Deng L, Yu D (2014) Deep Learning: Methods and Applications. Found Trends in Signal Process 7(3):197–387
R. B. Palm, "Prediction as a Candidate for Learning Deep Hierarchical Models of Data," Technical University of Denmark, 2012
B. Schölkopf, J. Platt, and T. Hofmann, "Greedy Layer-Wise Training of Deep Networks," in International Conference on Neural Information Processing Systems, 2006, pp. 153–160
R. J. Lewis, "An Introduction to Classification and Regression Tree (CART) Analysis," Annual Meeting of the Society for Academic Emergency Medicine, 2000
Maesschalck RD, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance. Chemom Intell Lab Syst 50(1):1–18
W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing, "Discovering spatio-temporal causal interactions in traffic data streams," in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 1010–1018
Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res C 54:187–197
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yu, G., Liu, J. A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion. Appl Intell 49, 1421–1436 (2019). https://doi.org/10.1007/s10489-018-1339-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-018-1339-3