Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network
Nowadays accurate and efficient traffic flow prediction is strongly needed by individual travelers and public transport management. Traffic flow prediction, especially long-term prediction, plays an important role in the application of intelligent transportation systems (ITS). In this paper, we propose a personalized design model (ResDeconvNN) based on Convolutional Neural Network (CNN) for long-term traffic flow prediction of elevated highways in Shanghai. The next whole day flow information can be predicted using the previous day flows. Taking the correlation of traffic parameters into account, we analogy flow, speed and occupancy (FSO) to the 3 channels of RGB as the 3 inputs of model. So the raw data collected from loop detectors are transformed into a spatial-temporal matrix which has 3 channels. Our model consists of two modules: Residual net and deconvolutional neural network. First, we take advantage of the residual net in deep network to extract the features of traffic. Then, we develop a deconvolutional network module and apply it to decode the flow of the next day from the comprehensive spatial and temporal traffic features. Experimental results indicate that the proposed model is robust and can achieve a better prediction accuracy compared with the other existing popular approaches.
KeywordsTraffic flow prediction ResDeconvNN model Intelligent transportation system
This work is supported by National Natural Science Foundation of China (No. 61876218, No. 61573259).
- 6.Sun, B., Cheng, W., Goswami, P., Bai, G.: Flow-aware wpt k-nearest neighbours regression for short-term traffic prediction. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 48–53. IEEE (2017)Google Scholar
- 8.More, R., Mugal, A., Rajgure, S., Adhao, R.B., Pachghare, V.K.: Road traffic prediction and congestion control using artificial neural networks. In: International Conference on Computing, Analytics and Security Trends (CAST), pp. 52–57. IEEE (2016)Google Scholar
- 9.Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)Google Scholar
- 11.Tan, H., Xuan, X., Wu, K, Zhong, Y.: A comparison of traffic flow prediction methods based on DBN. In: 16th COTA International Conference of Transportation, pp. 273–283 (2016)Google Scholar