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Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network

  • Di ZangEmail author
  • Yang Fang
  • Dehai Wang
  • Zhihua Wei
  • Keshuang Tang
  • Xin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

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.

Keywords

Traffic flow prediction ResDeconvNN model Intelligent transportation system 

Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (No. 61876218, No. 61573259).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Di Zang
    • 1
    Email author
  • Yang Fang
    • 1
  • Dehai Wang
    • 1
  • Zhihua Wei
    • 1
  • Keshuang Tang
    • 2
  • Xin Li
    • 3
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.Department of Transportation Information and Control EngineeringTongji UniversityShanghaiChina
  3. 3.Shanghai Lujie Electronic Technology Co., Ltd.Pudong, ShanghaiChina

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