Tarko 1 CALIBRATION OF SAFETY PREDICTION MODELS FOR PLANNING TRANSPORTATION NETWORKS

The current planning practice addresses safety implicitly as a byproduct of adding capacity and operational efficiency to the transportation system. Safety conscious planning is a new proactive approach to the prevention of crashes by establishing inherently safe transportation networks through integrating safety consideration into the transportation planning process. One of the major concerns in predicting crashes in transportation networks is the applicability and accuracy of crash prediction models. The paper presents two alternative formulations of the calibration problem consistent with the maximum likelihood approach. The proposed formulations can be viewed as a generalized version of the existing calibration procedure proposed in the past for individual crash prediction models. The proposed formulations are useful for road networks and for any transportation mode, provided that the needed prediction models are available. The proposed calibration applied to individual elements of the test network yielded crash estimates that exhibited a considerable bias accumulation at the system level. The calibration task was redefined to focus on the prediction of the cumulative number of crashes in the user-defined sub-networks. The second method gave more acceptable results. The paper demonstrates the feasibility of the proposed approach, which may be helpful in developing a new class of tools for safety conscious planning.