Missing Link Data Estimation with Recurrent Neural Network: An Application Using Speed Data of Daegu Metropolitan Area

Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

Abstract:

In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: Data Estimation, link data, machine learning, road network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316375

References:

[1] M. G. Karlaftis and E. I. Vlahogianni, “Statistical methods versus neural networks in transportation research: Differences, similarities and some insights,” Transp. Res. Part C Emerg. Technol., vol. 19, no. 3, 2011, pp. 387-399
[2] Zhong, M., Sharma, S., Lingras, P., “Genetically designed models for accurate imputation of missing traffic counts” Transportation Research Record 1879, 2004, pp. 71-79.
[3] Yin, H., Wong, S.C., Xu, J., “Urban traffic flow prediction using fuzzy neural approach”, Transportation Research Part C 10 (2), 2002, pp. 85-98.
[4] Teodorovic, D., Varadarajan, V., Jovan, P., Chinnaswamy, M., Sharath, R.,” Dynamic programming neural network real-time traffic adaptive signal control algorithm”, Annals of Operation Research 143 (1), 2006, pp. 123-131.
[5] Shmueli, D., Salomon, I., Shefer, D., 1996, “Neural network analysis of travel behavior: evaluating tools for prediction”, Transportation Research Part C: Emerging Technologies 4 (3), 1996, pp. 151-166.
[6] Nijkamp, P., Reggiani, A., Tritapepe, T., “Modelling inter-urban transport flows in Italy: a comparison between neural network approach and logit analysis”, Transportation Research Part C 4, 1996, pp. 323-338.
[7] Mozolin, M., Thill, J.C., Lynn Usery, E.L., “Trip distribution forecasting with multiplayer perceptron neural networks: a critical evaluation”, Transportation Research Part B 34 (1), 2000, pp. 53-73.
[8] Abdelwahab, H.T., Abdel-Aty, M.A., “Artificial neural networks and logit models for traffic safety analysis of toll plazas”, Transportation Research Record 1784, 2002, pp. 115-125.
[9] Abdel-Aty, M.A., Abdelwahab, H.T., “Predicting injury severity levels in traffic crashes: a modeling comparison”, Journal of Transportation Engineering 130 (2), 2004, 204-210
[10] G. P. Zhang and M. Qi, “Neural network forecasting for seasonal and trend time series,” Eur. J. Oper. Res., vol. 160, no. 2, 2005, pp. 501-514.
[11] M. Lee, "Forecasting short-term travel speed in a dense highway network considering both temporal and spatial relationship : using a deep-learning architecture," Chung-ang University, 2016
[12] Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C., “Spatio-temporal urban traffic volume forecasting using genetically-optimized modular networks”, Computer-aided Civil and Infrastructure Engineering 22 (5), 2007, 317-325
[13] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transp. Res. Part C Emerg. Technol., vol. 54, 2015, pp. 187-197.
[14] X. Ma, H. Yu, Y. Wang, and Y. Wang, “Large-scale transportation network congestion evolution prediction using deep learning theory,” PLoS One, vol. 10, no. 3, 2015, p. e0119044.
[15] Taehoon Kim, https://carpedm20.github.io/2014/neural-net-translation/
[16] T. H. Vu and J.-C. Wang, “Transportation Mode Detection on Mobile Devices Using Recurrent Nets,” Proc. 2016 ACM Multimed. Conf. - MM ’16, pp. 392–396, 2016.
[17] Team AI Korea, “Recurrent Neural Network (RNN) Tutorial – Part1” http://aikorea.org/blog/rnn-tutorial-1/