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Investigating the dynamics behind the likelihood of vehicle crashes has been a focal research point in the transportationsafety field for many years. However, the abundance of data in today's world generates opportunities for deepercomprehension of the various parameters affecting crash frequency. This study incorporates data from many differentsources including geocoded police-reported crash data, curbside infrastructure data and socio-demographic data for thecity of San Francisco, CA. Findings revealed that the GFMNB model provides a better statistical fit than the FMNB andNB model in terms of AIC and log likelihood, while the NB model outperformed both mixture models in terms of BIC dueto model complexity of the latter. Among the signicant variables, TNC pick-ups/dropoffs and duration of parked vehicleswere positively associated with segment-level crashes.