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.
Highlights
Intelligent Transportation Systems (ITS) require accurate knowledge of traffic states for effective traffic monitoring and control
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
Hao et al [7,8] recently proposed a model predictive control (MPC) termed urban cell transmission model (UCTM) for optimal switching of traffic lights at intersections based on the predicted traffic
Summary
Intelligent Transportation Systems (ITS) require accurate knowledge of traffic states for effective traffic monitoring and control. The problem of sparse data is caused by the high cost of installing and managing traffic measurement devices making them impractical to cover all locations needed for effective observation of the full road network. To address these challenges, researchers resorted to various methods and approaches such as missing data imputation [22], compressive sensing and historical averages [23], and Kriging interpolation [16]. Kriging methods were used to interpolate the missing data which is subsequently used for the computation of the PF likelihood for traffic state estimation.
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