A Recursive Data-driven Model for Traffic Flow Predictions for Locations with Faulty Sensors

Negin Alemazkoor, Shiyu Wang, Hadi Meidani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traffic flow prediction, as an integral part of intelligent transportation systems, is carried out by using data from the traffic sensors in the network. In a typical traffic prediction for a specific location, the most essential data has traditionally been the recent traffic flow records obtained from that same location. However, sensors are prone to failure, and as a result recent traffic measurements for the location of interest may be unavailable. In this work, we propose a model that predicts the traffic flow for locations with faulty sensors, solely by using traffic measurements from neighboring sensors. We use an online recursive regression approach to train the predictive model. We demonstrate the efficiency and accuracy of the proposed methodology using sensor data from California freeways. The results show that the developed model successfully predicts traffic flow with more than 95% accuracy on average.

Original languageEnglish (US)
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1646-1651
Number of pages6
ISBN (Electronic)9781728103235
DOIs
StatePublished - Dec 7 2018
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: Nov 4 2018Nov 7 2018

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-November

Other

Other21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
CountryUnited States
CityMaui
Period11/4/1811/7/18

Keywords

  • Faulty sensors
  • Online recursive regression
  • Traffic flow prediction

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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