Safety Prediction Models

Abstract

Urban transportation planning has traditionally focused on capacity and congestion issues with some attention paid to operation and management and with the treatment of such issues typically made proactively. In contrast, road safety has received little attention in the planning process. Safety-conscious planning is a new proactive approach that incorporates safety issues into the transportation planning process. This approach requires a safety planning decision-support tool to facilitate a proactive approach to the assessment of safety implications of alternative network planning initiatives and scenarios. The objective of this research study is to develop a series of zonal-level collision prediction models that are consistent with conventional models commonly used for urban transportation planning. A generalized linear regression modeling approach with the assumption of a negative binomial error structure was employed for exploring relationships between collision frequency in a planning zone and some explanatory variables such as traffic intensity, socioeconomic and demographic factors, land use, and traffic demand measures. Planning-level safety models developed in this study with data for the city of Toronto, Canada, are presented with illustrative applications of how they can be used as decision-support tools for planners to explicitly consider safety in the transportation planning process. Macrolevel collision modification factors are presented to illustrate how the models can be used to examine the impact of each individual planning variable on the safety of an urban zone.