Your browser does not support JavaScript!

Short-term travel-time prediction by deep learning: a comparison of different LSTM-DNN models

Short-term travel-time prediction by deep learning: a comparison of different LSTM-DNN models

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Traffic Information and Control — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Predicting short-term travel time with considerable accuracy and reliability is critically important for advanced traffic management and route planning in Intelligent Transportation Systems (ITS). Short-term travel-time prediction uses real travel-time values within a sliding time window to predict travel time one or several time step(s) in future. However, the nonstationary properties and abrupt changes of travel-time series make challenges in obtaining accurate and reliable predictions. Recent achievements of deep learning approaches in classification and regression shed a light on innovations of time series prediction. This study establishes a series of Long Short-Term Memory with Deep Neural Networks (LSTM-DNN) layers using 16 settings of hyperparameters and investigates their performance on a 90-day travel-time dataset from Caltrans Performance Measurement System (PeMS). Then competitive LSTM-DNN models are tested along with linear regression, Ridge and Lasso regression, ARIMA and DNN models under ten sets of sliding windows and predicting horizons via the same dataset. The results demonstrate the advantage of LSTM-DNN models while showing different characteristics of these deep learning models with different settings of hyper parameters, providing insights for optimizing the structures.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 Traffic time series estimation with deep learning
  • 4.2.1 Recurrent neural network
  • 4.2.2 Convolutional neural networks
  • 4.2.3 Generative adversarial networks
  • 4.3 The LSTM-DNN models
  • 4.4 Experiments
  • 4.4.1 Datasets
  • 4.4.2 Evaluation metrics
  • 4.4.3 Hyperparameter settings for LSTM-DNN models
  • 4.4.4 Comparison between LSTM-DNN models and benchmarks
  • 4.5 Conclusion and future work
  • References

Inspec keywords: traffic information systems; regression analysis; autoregressive moving average processes; neural nets; traffic engineering computing; learning (artificial intelligence); time series

Other keywords: time series prediction; accurate predictions; deep learning models; travel-time dataset; sliding time window; deep neural network layers; competitive LSTM-DNN models; travel-time series; short-term travel-time prediction; travel-time values; long short-term memory

Subjects: Neural computing techniques; Other topics in statistics; Knowledge engineering techniques; Traffic engineering computing

Preview this chapter:
Zoom in

Short-term travel-time prediction by deep learning: a comparison of different LSTM-DNN models, Page 1 of 2

| /docserver/preview/fulltext/books/tr/pbtr026e/PBTR026E_ch4-1.gif /docserver/preview/fulltext/books/tr/pbtr026e/PBTR026E_ch4-2.gif

Related content

This is a required field
Please enter a valid email address