Traffic Estimation for Large Urban Road Network with High Missing Data Ratio

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 12; p. 2813
• Main Authors: Offor, Kennedy John, Vaci, Lubos, Mihaylova, Lyudmila S
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24-06-2019; MDPI
• Subjects: Bayesian inference; Intelligent transportation systems; International conferences; Kriging; Kriging interpolation; Missing data; missing data imputation; particle filtering; road traffic; Sensors; Simulation; state estimation; System effectiveness; Traffic control; Traffic flow; Transportation networks; United Kingdom > UK; road traffic; state estimation; Bayesian inference; particle filtering; Kriging; missing data imputation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19122813

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.