Prediction aggregation of remote traffic microwave sensors speed and volume data

Short term traffic speed and volume prediction is an important component of well developed Intelligent Transportation Systems and Advanced Traveler Information Systems. In this paper, we examine the use of polled Remote Traffic Microwave Sensors as a data source for aggregate traffic predictors. Clock skew and data loss due to network transience pose significant challenges to integrating polled data into such a predictive system. To overcome these, we present a new interpolation and evaluation scheme for data regularization and predictor generation. A method for evaluating the validity of the test sets is proposed and illustrated in a case study using an aggregate predictor with real traffic sensor data acquired in Oklahoma City.