Abstract
An econometric framework was developed to combine data from various sources to identify the key factors contributing to travel time variations in Central London. Nonlinear latent variable regression models that explicitly accounted for measurement errors in the data were developed to combine data extracted from automatic number plate recognition cameras and automatic traffic counters. This procedure significantly differed from previous research in this area that was based primarily on traffic flow data and ignored measurement errors. The results indicate that nonlinear latent variable regression models can effectively explain travel time variations on a regular day by using variables related to vehicle type, traffic density, and traffic composition. Test results indicate that the proposed framework for correcting measurement errors yields significant improvements over base models, where such errors are ignored. The findings from the study validate some key hypotheses regarding influences of various factors on speed of urban traffic streams and can serve as a tool for investigation of the causes of traffic congestion. The model framework is general enough for application in other cases in which traffic data have similar measurement errors.
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Schedule a callMore From: Transportation Research Record: Journal of the Transportation Research Board
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