The Real-Time Road Traffic Signal Light Assignment Strategy Prediction Based on Deep Learning

Due to the quickly developing economy and improving living standards, it has become a problem that urban traffic roads are not able to meet the needs of such a great amount of motor vehicles. The condition of a traffic system is sensitive to the distribution of traffic flow, which can be directly led by the signal lamps. In this study, the authors propose a novel architecture of neuron network, CNN-LSTM (convolution neuron network-long short-term neuron network), which puts both spatial and temporal corresponding into consideration. A deep convolutional neuron network is utilized to capture the features among data in different lanes and a long short-term memory neuron network is used to capture the temporal features in time sequences. A classifier is applied to determine which assignment strategy to choose. A comparison with other models suggests that the authors' deep learning method is superior to other methods with high accuracy.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

Filing Info

  • Accession Number: 01715144
  • Record Type: Publication
  • ISBN: 9780784482292
  • Files: TRIS, ASCE
  • Created Date: Jul 2 2019 3:08PM