Abstract

Crash prediction models used to estimate safety at intersections, road, and highway segments are traditionally developed using various traffic volume measures. There are issues with this approach and surrogate safety measures such as conflicts and delays can overcome them. This study investigates the relationships between crash frequencies and traffic volume, intersection delay, and conflicts to explore the viability of these models for estimating safety at two-way stop controlled intersections. The database used includes 78 three leg and 55 four leg intersections within the Greater Toronto Area. Crash prediction models were developed and evaluated based on goodness-of-fit measures and CURE plots. Two conflict estimation techniques are compared in order to determine which is best suited for two-way stop controlled intersection simulations. This study also investigates the use of the models for estimating the safety impact of implementing a left turn lane on a major approach of a three leg intersection.

Highlights

  • Worldwide the number of fatalities due to road traffic crashes each year is estimated at approximately 1.2 million, and the number injured could be as high as 50 million (World Health Organization, 2004)

  • Any conflicts with zero values of time to crash (TTC) and Post Encroachment Time (PET) were removed as these conflicts are caused by errors in the simulation

  • The Mean Squared Error (MSE)/Year and Mean Squared Prediction Error (MSPE)/Year values are equal which indicates that the models are a good fit generally to the estimation data

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Summary

Introduction

Worldwide the number of fatalities due to road traffic crashes each year is estimated at approximately 1.2 million, and the number injured could be as high as 50 million (World Health Organization, 2004). Crash prediction models are commonly developed in order to evaluate and improve traffic safety at roadways and intersections. These models are developed with historical crash data and statistics. Applying a generalized linear regression modelling approach, the crash frequency is correlated with selected explanatory variables These crash prediction models can be used by safety analysts to evaluate safety at roadways and intersections. A study conducted by Sayed and Rodriguez (1999) developed crash prediction models relating crashes and traffic volume for 419 (186 three leg and 233 four leg) urban two-way stop controlled intersections in British Columbia. The resulting crash prediction models showed satisfactory goodness-of-fit (Sayed & Rodriguez, 1999) This conventional study illustrates the modelling of crashes and traffic volumes; it does not explore other explanatory variables. In some cases traffic volume alone will not provide adequate crash prediction models, the use of other explanatory variables should be explored

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