Network Scale Travel Time Prediction using Deep Learning

Yi Hou, Praveen Edara

Research output: Contribution to journalArticlepeer-review

39 Scopus Citations


In recent years, deep learning models have been receiving increased attention within the artificial intelligence (AI) community because of their high prediction accuracy. In this paper, two deep learning models, long short-term memory (LSTM) and convolutional neural network (CNN), are proposed to predict travel time in a road network. One major advantage of using deep learning for travel time prediction is that it can make accurate predictions for all the segments in the transportation network with a single model structure, instead of building customized models for each segment separately. The proposed models were evaluated on a transportation network in the City of Saint Louis, Missouri. The prediction results show that deep learning can provide accurate prediction for both congested and uncongested traffic conditions, and can successfully capture the traffic dynamics of unexpected incidents or special events. The study findings show that deep learning offers a promising approach to real-time prediction of travel times on a network scale.
Original languageAmerican English
Pages (from-to)115-123
Number of pages9
JournalTransportation Research Record
Issue number45
StatePublished - 2018

NREL Publication Number

  • NREL/JA-5400-71974


  • forecasting
  • long short-term memory
  • motor transportation
  • time varying control systems
  • traffic control
  • travel time


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