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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 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.

This paper draws on advances in spatial networks by representing a city as a weighted primal graph of a street network, which takes into account the context of location and its importance. We introduce the link-based multiple centrality indices (L-MCI) to represent location properties in terms of closeness, intermediacy, straightness and accessibility to all other locations. The proposed methodology is built on concepts of multiple centrality assessment (MCA) model. Results from L-MCI clearly identify the major city centers in Santa Barbara County based on the geometric configuration of the network. Moreover, these centrality indices also exhibit some unique properties that can be observed across other network structures. We also employ a clustering technique that accounts for spatial dependence in centrality values across multiple spatial scales, which aids in classifying the region into locations of high centrality and low centrality. We further demonstrate the novelty of this approach in examining the relationship between structural properties of the street network and spatial organization of economic activities in Santa Barbara County, California. Results from this study confirm that link-based network centrality indices play a significant role in spatial distribution of economic activities. Professional services and retail trade form a major proportion of economic activities in locations with very high centrality values, e.g. downtown areas. Locations with high betweenness centrality values are especially attractive to retail trade activities, as they generate a greater potential for business opportunities. The results clearly reveal the presence of core-periphery type of a city model in Santa Barbara County.