Comparison of Modeling Approaches for Imputation of Video Detection Data in Intelligent Transportation Systems

Abstract

Gaps in real-time and archived traffic data are common and can be attributed to several factors, such as sensor failures and data communications interruptions. Regardless of the cause of the missing data, these gaps often must be filled with reliable and accurate estimates before the data can be used for planning, operations, or congestion-mitigation purposes. This research compared different methods for imputing missing values in video detection system data, including historical averages, simple linear regression, multiple linear regression, spatial averages, and Newell's simplified kinematic wave model. The study used the fundamental relationship between speed and flow in filtering the data for quality control. A sensitivity analysis tested the response of different methods to factors such as the size of training data set and time-of-day adjustments to the algorithms. The results indicated that the time of day and volume adjustment factors had a nontrivial impact on the accuracy of the outputs. Despite significant errors in the base data set, the Newell algorithm performed on a par with the other methods in terms of bias and mean absolute percentage, but the more simple factoring methods provided comparable results and were easier to implement.