Abstract
The problem of short-term travel time estimation has been intensively investigated recently. However, accurate travel time predicting is still a challenge due to dynamic changes of the traffic and the difficulty of extracting urban traffic data features. In this paper, we mainly focus on time shifting feature of urban roads, which represents the impact of the upstream sections that will be conveyed to the downstream sections after a certain period of time \(\varDelta {t}\). Firstly, we obtain the spatial relationships of the traffic time with Kullback-Leibler divergence (KL-divergence) and urban road networks. Then a Convolutional Neural Network (CNN) module is adopted to extract the spatial-temporal and time shifting information of the target road. Finally, a novel deep architecture combined CNN and Long-short Term Memory Recurrent Neural Network (LSTM) is utilized to predict the short-term travel time. The experimental result on the real data set shows that the proposed model is more effective than other existing approaches.
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Acknowledgment
This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2015CB352502, the National Natural Science Foundation of China under Grant Nos. 61272092 and 61572289, the Natural Science Foundation of Shandong Province of China under Grant Nos. ZR2012FZ004 and ZR2015FM002, the Science and Technology Development Program of Shandong Province of China under Grant No. 2014GGE27178, and the NSERC Discovery Grants.
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Wei, W., Jia, X., Liu, Y., Yu, X. (2018). Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_25
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DOI: https://doi.org/10.1007/978-3-319-96890-2_25
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