Prepared by:

Alireza Hadayeghi
Transportation Safety Analyst
Synectics Transportation Consultants Inc.
St. Catharines, Ontario

Amer S. Shalaby
Assistant Professor of Civil Engineering
University of Toronto
Toronto, Ontario, Canada

Bhagwant N. Persaud
Professor of Civil Engineering
Ryerson University
Toronto, Ontario, Canada

Abstract

The objective of this study was to develop a series of macro-level prediction models that would estimate the number of accidents in planning zones in the City of Toronto as a function of zonal characteristics. A generalized linear modeling approach was employed in which Negative Binomial regression models were developed separately for total accidents and for severe (fatal and non fatal injury) accidents as a function of socioeconomic/demographic, traffic demand and network data variables. The variables that had significant effects on accident occurrence were the number of households, major road kilometers, vehicle kilometers traveled, intersection density, posted speed and volume-capacity ratio. The Geographic Weighted Regression (GWR) approach was employed to test spatial variations in the estimated parameters from zone to zone. Mixed results were obtained from that analysis.

For a complete copy of this paper, please contact: jsuggett@synectics-inc.net