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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 …