Offor Kennedy John, Vaci Lubos, Mihaylova Lyudmila S
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK.
Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK.
Sensors (Basel). 2019 Jun 24;19(12):2813. 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.
智能交通系统需要了解当前和预测的交通状态，以便对道路网络进行有效控制。由于现有的传感器无法捕捉到所需的状态，因此必须估计实际交通状态。由于通信问题、传感器故障或成本等原因，传感器测量数据往往包含缺失或不完整的数据，导致对整个道路网络的监测不完整。这种缺失的数据给交通估计方法带来了挑战。在这项工作中，提出了一种能够承受高缺失数据率的鲁棒时空交通插补方法。提出了一种基于粒子的克里金插值方法。针对一个由1000个路段组成的大型道路网络，研究了基于粒子的克里金插值在不同缺失数据率下的性能。结果表明，在粒子滤波框架内，克里金插值可以减轻大型道路网络中缺失数据的影响。
