Predicting Operating Speeds on Low-Speed Urban Streets: Regression and Panel Analysis Approaches

Abstract

This study compares different statistical approaches to modeling the geometric and driver effects on operating speeds along low-speed urban streets. Linear regression on speed data obtained through data aggregation, linear regression on individual speed data, and panel analysis are discussed. Data collected from ongoing research examining operating speed on low-speed urban streets were modeled by the three techniques. The findings of the modeling techniques are compared and their influence on predicting probable operating speeds of a facility are presented. Traditionally, empirical analysis of operating speed has relied on regression models, using descriptive statistics such as 85th-percentile speed or mean speed to describe the data. This study demonstrates how the use of descriptive statistics obtained through data aggregation misleadingly reduces the total variability and nature of the variability associated with the statistical relationship. The fit of the regression function may appear to be increased, but the influence of the geometric elements may be overstated or understated. Data aggregation also affects inferential and prediction measures. Predictions from models based on aggregate data may appear to be more precise, but this does not imply that they are more reliable. Regression models of speed choice at a specific location within the roadway alignment may explain the effect of geometry but may not capture the effect of individual driver speed choice. As demonstrated in this study, the individual driver effect and geometric variable effect are important. The preliminary conclusion is that the driver's speed choice is highly dependent on roadway geometry and individual driver behavior.