Abstract
The aim of this study was to evaluate and identify the factors influencing operating speed considering context classification. The study focused on three context classifications: C3R–Suburban Residential, C3C–Suburban Commercial, and C4–Urban General. Tobit models were proposed and developed using big data, including traffic and roadway characteristics, land-use attributes, and sociodemographic information. Three years of INRIX speed data were obtained to calculate the 85th-percentile speed. The study proposed an approach to adjust the 85th-percentile speed from INRIX data, given that traffic flow on arterials could be disrupted by signalized intersections. Afterward, empirical analysis was conducted by developing three Tobit models: Generic, C3C/C3R, and C4 models using the adjusted 85th-percentile speed. For the three developed models, several variables [e.g., inside shoulder width, speed limit, and number of signalized intersections per mile (1.609 km)] were found to have significant influence on the 85th-percentile speed. The analysis also revealed potential speed management countermeasures that have significant impact on the 85th-percentile speed which, when implemented, could reduce speed-related crashes and enhance the safety of vulnerable road users.
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