Citation

Offor KJ, Vaci L, Mihaylova LS. Sensors (Basel) 2019; 19(12): s19122813.

Affiliation

Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. l.s.mihaylova@sheffield.ac.uk.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19122813

PMID

31238533

Abstract

Intelligent transportation systems require the knowledge of current and forecasted traffic states for effective control of road networks. The actual traffic state has to be estimated as the existing sensors does not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data poses challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle based approach with Kriging interpolation is proposed. The performance of the particle based Kriging interpolation for different missing data ratios was investigated for a large road network comprising 1000 segments.

RESULTS indicate that the effect of missing data in a large road network can be mitigated by the Kriging interpolation within the particle filter framework.

Language: en

Keywords

Bayesian inference; Kriging; missing data imputation; particle filtering; road traffic; state estimation