01023081

Component

https://doi.org/10.3141/1950-10

http://scholar.google.com...arko&publication_year=2006

The current planning practice addresses safety implicitly as a by-product of adding capacity and operational efficiency to the transportation system. Safety-conscious planning is a new proactive approach to the prevention of crashes based on establishing inherently safe transportation networks through integrating consideration of safety 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 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 subnetworks. 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.

01030706

06-1451

English

pp 83-91

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099595

Print

Figures (3) ; References (17) ; Tables (5)

Calibration; Crash risk forecasting; Highway safety; Maximum likelihood method; Network analysis (Planning); Planning methods; Transportation planning

Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I81: Accident Statistics

TRIS, TRB, ATRI

Mar 3 2006 10:41AM

• Application of "Highway Safety Manual" Draft Chapter: Louisiana Experience
• Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
• Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models
• Describing the Evolution in the Number of Highway Deaths by Decomposition in Exposure, Accident Risk, and Fatality Risk
• Effects of Sample Size on Goodness-of-Fit Statistic and Confidence Intervals of Crash Prediction Models Subjected to Low Sample Mean Values
• Refinement of Accident Prediction Models for Spanish National Network
• Use of Propensity Score Matching Method and Hybrid Bayesian Method to Estimate Crash Modification Factors of Signal Installation
• Using Macrolevel Collision Prediction Models in Road Safety Planning Applications
• Using the Rural Two-Lane Highway Draft Prototype Chapter