Imputing Erroneous Data of Single-Station Loop Detectors for Nonincident Conditions: Comparison Between Temporal and Spatial Methods

Abstract

Effective use of loop detector data for traffic management requires that errors be efficiently detected, diagnosed, and corrected. We present two new spatial approaches and compare them to state-of-the-art correction procedures for station flow estimation when detectors within that station malfunction in nonincident conditions. One new method exploits the relationship between individual detector flow and station flow using linear regression. The second incorporates lane use percentages through kernel regression. To comprehensively compare the procedures, systematic and random-error evaluations are conducted for two detector stations with distinct lane configurations. Lane configuration is important for spatial correction methods, which perform well under certain detector failure combinations. The random-error evaluation indicates that temporal correction performs better at all error levels and spatial approaches are inaccurate under light traffic conditions, especially when estimates are based on zero flow readings. When choosing a correction procedure, one should consider facility configurations, error types and magnitudes, and traffic conditions, and calibrate the method for location-specific characteristics.