A Long-Term Highway Traffic Flow Prediction Method for Holiday

Due to the erratic fluctuation of holiday traffic, it is hard to make accurate prediction for holiday traffic flow. This paper introduces the fluctuation coefficient method, which is widely used in passenger flow management, to holiday traffic flow prediction. Based on the analysis of the characteristics of traffic flow, we divid holiday traffic flow into regular and fluctuant parts. The regular flow is predicted by Long Short-term Memory Model, and the fluctuant flow is forecasted by fluctuation coefficient method. This method can overcome the shortage of historical data, and the effectiveness of this method is verified by the experiments.

[1]  Luc J. J. Wismans,et al.  Scalable data-driven short-term traffic prediction , 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[2]  Roberto Horowitz,et al.  Multiple-clustering ARMAX-based predictor and its application to freeway traffic flow prediction , 2014, 2014 American Control Conference.

[3]  Yan Huang,et al.  Multicast capacity analysis for social-proximity urban bus-assisted VANETs , 2013, 2013 IEEE International Conference on Communications (ICC).

[4]  Guojie Song,et al.  A short-term freeway traffic flow prediction method based on road section traffic flow structure pattern , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[5]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  Zhipeng Cai,et al.  An Urban Area-Oriented Traffic Information Query Strategy in VANETs , 2013, WASA.

[7]  Yang Wen,et al.  A dynamic traffic assignment model for highly congested urban networks , 2012 .

[8]  Tharam S. Dillon,et al.  Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Jian-Min Xu,et al.  Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN , 2017, 2017 Chinese Automation Congress (CAC).

[11]  Jianzhong Li,et al.  An Application-Aware Scheduling Policy for Real-Time Traffic , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[12]  Zhongsheng Hou,et al.  Repeatability and Similarity of Freeway Traffic Flow and Long-Term Prediction Under Big Data , 2016, IEEE Transactions on Intelligent Transportation Systems.