Traffic Flow Prediction Based on Hybrid Deep Learning Under Connected and Automated Vehicle Environment

被引：0
|
作者：
Lu W.-Q. [1 ,2 ,3 ]
Rui Y.-K. [1 ,2 ,3 ]
Ran B. [1 ,2 ,3 ]
Gu Y.-L. [4 ]
机构：
[1] School of Transportation, Southeast University, Nanjing
[2] Joint Research Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing
[3] Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing
[4] Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing
来源：
Ran, Bin (bran@seu.edu.cn) | 1600年 / Science Press卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Ensemble empirical mode decomposition; Hybrid deep learning; Intelligent transportation; Speed prediction; Traffic flow;
D O I：
10.16097/j.cnki.1009-6744.2020.03.008
中图分类号：
学科分类号：
摘要：
To achieve refined traffic flow prediction under connected and automated vehicle highway (CAVH) environment, this study proposes a lane-level traffic flow prediction model based on the hybrid deep learning (HDL). The proposed method takes the advantages of powerful data collection and calculation capability of the CAVH system. The HDL model divided the raw traffic speed series into several intrinsic mode function components and one residual component, and used the components as the input of the model. The bidirectional long short-term memory neural network and attention mechanism were used to establish the framework of the deep learning model. The lane-level speeds of the 2nd Ring road in Beijing, China were utilized to examine the accuracy and reliability of the proposed model. The results illustrate that the HDL model has ideal prediction performance at different types of lanes. Meanwhile, the prediction accuracy of the HDL model is significantly higher than that of previous models in terms of single-step-ahead prediction and multi-step-ahead prediction. Copyright © 2020 by Science Press.
引用
收藏
页码：47 / 53
页数：6
相关论文
共 10 条
  • [1] RAN B, TAN H C, ZHANG J, Et al., Development status and trend of connected automated vehicle highway system, Journal of Automotive Safety and Energy, 9, 2, pp. 5-16, (2018)
  • [2] VAN DER VOORT M, DOUGHERTY M, WATSON S., Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transportation Research Part C: Emerging Technologies, 4, 5, pp. 307-318, (1996)
  • [3] YU Q, YAO Z H., Markov particle filter traffic flow prediction model, Journal of Transportation Systems Engineering and Information Technology, 19, 2, pp. 213-219, (2019)
  • [4] TERESA P., Road traffic conditions classification based on multilevel filtering of image content using convolutional neural networks, IEEE Intelligent Transportation Systems Magazine, 10, 3, pp. 11-21, (2018)
  • [5] MA X, TAO Z, WANG Y, Et al., Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transportation Research Part C: Emerging Technologies, 54, pp. 187-197, (2015)
  • [6] WANG X X, XU L H., Short-term traffic flow prediction based on deep learning, Journal of Transportation Systems Engineering and Information Technology, 18, 1, pp. 81-88, (2018)
  • [7] GU Y L, LU W Q, LI M, Et al., Lane-level traffic speed prediction for expressways based on a combined deep learning model, Journal of Transportation Systems Engineering and Information Technology, 19, 4, pp. 79-86, (2019)
  • [8] WU Z, HUANG N., Ensemble empirical mode decomposition: A noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1, 1, pp. 1-41, (2009)
  • [9] HUANG N, SHEN Z, LONG S R, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454, pp. 903-995, (1998)
  • [10] HOCHREITER S, URGEN SCHMIDHUBER J., Long short-term memory, Neural Computation, 9, 8, pp. 1735-1780, (1997)
1