Browse > Article
http://dx.doi.org/10.12815/kits.2021.20.1.86

Speed Prediction of Urban Freeway Using LSTM and CNN-LSTM Neural Network  

Park, Boogi (Dept. of Spatial Information Eng., Pukyong National University)
Bae, Sang hoon (Dept. of Spatial Information Eng., Pukyong National University)
Jung, Bokyung (Dept. of Spatial Information Eng., Pukyong National University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.1, 2021 , pp. 86-99 More about this Journal
Abstract
One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.
Keywords
Traffic prediction; Traffic analysis; Traffic incident impact area; Artificial neural network; Uninterrupted flow;
Citations & Related Records
연도 인용수 순위
  • Reference
1 ALIO(2013), Korea Road Traffic Authority Traffic Science Institute, pp.86-87.
2 FHWA(2006), Traffic Detector Handbook, pp.3-17.
3 Jung H. J., Yoon J. S. and Bae S. H.(2017), "Traffic Congestion Estimation by Adopting Recurrent Neural Network," The Journal of the Korea Institute of Intelligent Transport Systems, vol. 16, no. 6, pp.67-78.   DOI
4 Kim D. H., Hwang K. Y. and Yoon Y.(2019), "Prediction of Traffic Congestion in Seoul by Deep Neural Network," The Journal of the Korea Institute of Intelligent Transport Systems, vol. 18, no. 4, pp.44-57.
5 Korea Meteorological Administration Data Center ASOS Data, https://data.kma.go.kr, 2020.07.27.
6 Lee H. S. and Bui K. H. N.(2019), "Deep Learning LSTM for Long-Short Term Traffic Flow Prediction," The Korean Institute of Information Scientists and Engineers 2019, pp.724-726.
7 Park S. H., Choi D. J., Bok K. S. and Yoo J. S.(2020), "Road Speed Prediction Scheme Considering Traffic Incidents," The Journal of the Korea Contents Association, vol. 20, no. 4, pp.25-37.   DOI
8 Sepp H. and Jurgen S.(1997), "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp.1735-1780.   DOI
9 Simard P. Y., Steinkraus D. and Platt J. C.(2003), "Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis," Proceedings of the 7th International Conference on Document Analysis and Recognition, no. 3, pp.958-963.
10 The Korea's Transport Institute Press Release, https://www.koroad.or.kr, 2020.10.13.
11 Traffic Accident Analysis System, http://taas.koroad.or.kr, 2020.10.13.
12 Zheng H., Lin F., Feng X. and Chen Y.(2020), "A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction," IEEE Transactions on Intelligent Transportation Systems, pp.1-11.