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Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction (2112.02409v2)

Published 4 Dec 2021 in cs.LG and cs.AI

Abstract: Reliable traffic flow prediction is crucial to creating intelligent transportation systems. Many big-data-based prediction approaches have been developed but they do not reflect complicated dynamic interactions between roads considering time and location. In this study, we propose a dynamically localised long short-term memory (LSTM) model that involves both spatial and temporal dependence between roads. To do so, we use a localised dynamic spatial weight matrix along with its dynamic variation. Moreover, the LSTM model can deal with sequential data with long dependency as well as complex non-linear features. Empirical results indicated superior prediction performances of the proposed model compared to two different baseline methods.

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References (35)
  1. Statistical characteristics of transitional queue conditions in signalized arterials. Transportation Research Part C: Emerging Technologies, 15(6):392–403, 2007.
  2. Deep learning for monaural speech separation. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1562–1566. IEEE, 2014.
  3. Travel-time prediction with deep learning. In 2016 IEEE Region 10 Conference (TENCON), pages 1859–1862. IEEE, 2016.
  4. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2):865–873, 2014.
  5. Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies, 19(4):606–616, 2011.
  6. Traffic speed prediction and congestion source exploration: A deep learning method. In 2016 IEEE 16th international conference on data mining (ICDM), pages 499–508. IEEE, 2016a.
  7. Lstm network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2):68–75, 2017.
  8. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(4):389–413, 2012.
  9. A dynamic spatial weight matrix and localized space–time autoregressive integrated moving average for network modeling. Geographical Analysis, 46(1):75–97, 2014.
  10. Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Number 722. 1979.
  11. On forecasting freeway occupancies and volumes (abridgment). Transportation Research Record, (773), 1980.
  12. Billy M Williams. Multivariate vehicular traffic flow prediction: evaluation of arimax modeling. Transportation Research Record, 1776(1):194–200, 2001.
  13. Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of transportation engineering, 129(6):664–672, 2003.
  14. A hidden markov model for short term prediction of traffic conditions on freeways. Transportation Research Part C: Emerging Technologies, 43:95–111, 2014.
  15. New bayesian combination method for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 43:79–94, 2014.
  16. Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences. IET intelligent transport systems, 6(3):292–305, 2012.
  17. Non-parametric regression for space–time forecasting under missing data. Computers, Environment and Urban Systems, 36(6):538–550, 2012.
  18. Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. In Proceedings of IEEE international conference on systems, man and cybernetics, volume 2, pages 1706–1709. IEEE, 1994.
  19. Traffic volume time-series analysis according to the type of road use. Computer-Aided Civil and Infrastructure Engineering, 15(5):365–373, 2000.
  20. Hussein Dia. An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, 131(2):253–261, 2001.
  21. Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one, 10(3):e0119044, 2015a.
  22. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies, 62:21–34, 2016.
  23. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54:187–197, 2015b.
  24. Predicting road system speeds using spatial structure variables and network characteristics. Journal of Geographical Systems, 9(4):397–417, 2007.
  25. Discovering spatio-temporal causal interactions in traffic data streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1010–1018, 2011.
  26. Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research Part C: Emerging Technologies, 13(5-6):347–369, 2005.
  27. A space–time delay neural network model for travel time prediction. Engineering Applications of Artificial Intelligence, 52:145–160, 2016b.
  28. On the difficulty of training recurrent neural networks. In International conference on machine learning, pages 1310–1318. PMLR, 2013.
  29. Learning natural language inference with lstm. arXiv preprint arXiv:1512.08849, 2015.
  30. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. Ieee, 2013.
  31. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
  32. Tianchi. Kdd cup 2017 highway tollgates traffic flow prediction dataset, 2018. URL https://tianchi.aliyun.com/dataset/dataDetail?dataId=60.
  33. Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 43:3–19, 2014.
  34. Matthew D Zeiler. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.
  35. A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transportation Research Part C: Emerging Technologies, 19(6):1306–1318, 2011.
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Authors (3)
  1. Won Kyung Lee (1 paper)
  2. Deuk Sin Kwon (3 papers)
  3. So Young Sohn (1 paper)

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