Abstract
Safety conscious planning is a new proactive approach towards understanding crashes. It requires a planning-level decision-support tool to facilitate proactive approach to assessing safety effects of alternative urban planning scenarios. The objective of this research study is to develop a series of aggregate crash prediction models (ACPM) that are consistent with the trip generation step of the conventional four-step demand models. The concept of crash generation models (CGMs) is introduced utilizing trip generation data in a generalized linear regression with the assumption of a negative binomial error structure. The relationship of crash frequencies in traffic analysis zones (TAZ) and number of trips generated by purpose is investigated. This translates into immediate checking of the impact of future trip generations on crash frequencies in comprehensive transportation-planning studies (i.e. ability to forecast crashes at each time-step trips are being forecasted). A good relation was seen between crash frequency and number of trips produced/attracted by purpose per TAZ.
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