Predicting Traffic Volume and Occupancy at Failed Detectors

Ishan Tarunesh, Edward Chung

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

Accurate Traffic flow prediction relies on correctness of the values received from detectors. It is often the case that detectors are not working correctly and provide with incorrect values. The aim of this work is to predict the traffic flow variables at the failed detectors using deep learning techniques such as neural network and autoencoders. The major contributions are using neural network to model the complete network of detectors and use of autoencoders to reduce model size by exploiting spatial correlation between detectors. To the best of our knowledge deep learning has never been applied incase of detector failure.

Original languageEnglish
Pages (from-to)1072-1083
Number of pages12
JournalTransportation Research Procedia
Volume48
DOIs
Publication statusPublished - 2020
Event2019 World Conference Transport Research, WCTR 2019 - Mumbai, India
Duration: 26 May 201931 May 2019

Keywords

  • autoencoder
  • machine learning
  • neural network
  • occupancy prediction
  • Traffic volume prediction

ASJC Scopus subject areas

  • Transportation

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