Online Method to Impute Missing Loop Detector Data for Urban Freeway Traffic Control

Abstract

The dual loop detector provides input data for real-time traffic control. However, the common scenario of missing or invalid loop data samples negatively impacts the performance of the subsequent traffic control or traffic prediction model. This paper describes a method that detects blank data samples when a new data record is listed, then imputes missing data samples to form a complete data record in real time. One three-lane loop detector station on Whitemud Drive in Edmonton, Alberta, Canada, is used as a case study to verify the algorithms. The imputation scope for this study involves diagnosing and filling in the missing data of one lane for at least one whole day at a specific loop station. The diagnosis of missing data for both volume and speed measurements is based on data records from upstream and downstream stations, rather than from single samples. The imputation algorithm for volume and density data models the relationship of one loop detector with all other loop detectors at the same station as linear. The multiple linear regression algorithm is applied to full historic data offline to learn the linear relationship between loops at the same station, and the online data imputation is based on the equations learned. Two other commonly used imputation techniques—pairwise linear regression and the average of the surrounding detectors—are also conducted for comparison. The proposed diagnosis and imputation method enables the loop data input to support control models with high frequency and large data requirements.