Traffic Estimation for Large Urban Road Network with High Missing Data Ratio
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 da...
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doaj-437a3591e0e5475997c7465d0c27e0a82020-11-25T01:40:37ZengMDPI AGSensors1424-82202019-06-011912281310.3390/s19122813s19122813Traffic Estimation for Large Urban Road Network with High Missing Data RatioKennedy John Offor0Lubos Vaci1Lyudmila S. Mihaylova2Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKDepartment of Computer Science, University of Sheffield, Sheffield S1 4DP, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UKIntelligent 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.https://www.mdpi.com/1424-8220/19/12/2813particle filteringroad trafficstate estimationBayesian inferenceKrigingmissing data imputation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kennedy John Offor Lubos Vaci Lyudmila S. Mihaylova |
spellingShingle |
Kennedy John Offor Lubos Vaci Lyudmila S. Mihaylova Traffic Estimation for Large Urban Road Network with High Missing Data Ratio Sensors particle filtering road traffic state estimation Bayesian inference Kriging missing data imputation |
author_facet |
Kennedy John Offor Lubos Vaci Lyudmila S. Mihaylova |
author_sort |
Kennedy John Offor |
title |
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio |
title_short |
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio |
title_full |
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio |
title_fullStr |
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio |
title_full_unstemmed |
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio |
title_sort |
traffic estimation for large urban road network with high missing data ratio |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-06-01 |
description |
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. |
topic |
particle filtering road traffic state estimation Bayesian inference Kriging missing data imputation |
url |
https://www.mdpi.com/1424-8220/19/12/2813 |
work_keys_str_mv |
AT kennedyjohnoffor trafficestimationforlargeurbanroadnetworkwithhighmissingdataratio AT lubosvaci trafficestimationforlargeurbanroadnetworkwithhighmissingdataratio AT lyudmilasmihaylova trafficestimationforlargeurbanroadnetworkwithhighmissingdataratio |
_version_ |
1725044590979842048 |