|This report is an archived publication and may contain dated technical, contact, and link information|
Publication Number: FHWA-RD-03-037
Date: May 2005
Validation of Accident Models for Intersections
FHWA Contact: John Doremi,
PDF Version (1.61 MB)
PDF files can be viewed with the Acrobat® Reader®
The research described in this report consists of two separate yet complimentary activities-validation and calibration of crash models for rural intersections. Both the validation and recalibration activities were conducted with one overriding research objective:
Given existing database limitations, make marginal improvements to an existing set of statistical models for predicting crashes at two- and four-lane intersections, with the primary intent to provide robust predictive models for use in the Interactive Highway Safety Design Module (IHSDM).
The five types of intersection models addressed in this research effort include:
The models that are the focus of this research are presented in three different Federal Highway Administration (FHWA) reports: Vogt and Bared (Types I and II);(1) Vogt (Types III, IV, and V);(2) and Harwood et al. (Types I, II, and V).(3) Each report presents several variants of the models for each type of intersection. The first two reports include models for total as well as injury accidents and present what are referred to as full models. The Harwood et al. report presents base models for Types I, II, and V intersections.(3) These base models included variables that were statistically significant at the 15 percent level and are at the backbone of an algorithm for predicting accidents at intersections that are different in one or more features from the specified base conditions. Specifically, accident modification factors (AMF) for the features of interest are applied to the base model prediction to estimate accidents per unit of time for a specific intersection. This algorithm is intended for use in the Crash Prediction Module of FHWA's IHSDM. The anticipated practical application of these models has motivated research directions taken throughout the course of this investigation.
The data in support of this research were derived from three sources:
The research team faced a number of challenges while conducting this research, including data collection, independent variable characteristics, and the models' intended end-use:
Despite these challenges, the research team conducted a model validation and then recalibrated the five intersection models.
The four sets of validation activities were:
Two basic sets of performance tests were employed. First, the models were re-estimated using the same variables and functional forms as those published in the original reports; the parameters for the original and re-estimated models were then compared, using a level of alpha = 0.10 to establish statistical significance. Second, the model (or algorithm) was used to predict accident frequencies at individual intersections, from which the following summary statistics were calculated:
The details of validation activities 1 through 4 are presented in sections 3.4 through 3.7 respectively, while the results are discussed in section 3.8.
Model recalibration was focused on improving the existing set of intersection crash models through use of an improved and expanded database and through lessons learned in the validation and recalibration activities.
For each the five intersection types, the research team developed and/or refined three different sets of models, described in detail in chapter 3. The first type is Annual Average Daily Traffic (AADT) Models, which represent base models for predicting crashes as a function of major and minor road AADT. The analytical results of these models can be found in subsequent sections of this report. The second type of model is Full Models. These statistical models forecast crashes as a function of a relatively large set of independent variables. Details of the Full Models can be found in section 3.4 of this report. The third type of model is AMF. These models, better described as countermeasure correction factors, represent our best efforts to estimate the effect of geometric countermeasures on safety relative to base model predictions. AMF details can be found in section 3.5 of this report.
Sensitivity analyses-tables of AMFs as a function of AADT and other factors are provided in section 3.6.
The research supported the proposed IHSDM accident prediction algorithm. An updated set of base models for predicting crashes using only AADT are recommended (see Summary, Discussion, and Conclusions section and Table 235 ). The updated statistical models are based on larger sample sizes and, in some cases, resulted in slightly modified sets of independent variables compared to the originally estimated models. AMFs should be selected on a case-by-case basis, and should be updated continually to improve the predictive ability of the crash models. Expert opinion derived AMFs should be replaced with the results of state-of-the-practice before-after studies as time progresses and research allows. If expert opinion accurately reflects safety conditions, then carefully conducted future studies should reveal general agreement with expert expectation. When expert opinions are not confirmed over time, then empirical results should replace expert opinion. Full regression models are recommended for crash forecasts and find logical applications in the Highway Safety Manual and Safety Analyst (see Summary, Discussion, and Conclusions section and Table 236 ).
Topics: research, safety, intersection safety
Keywords: research, safety, Accident modification factors, Traffic safety, Signalized intersections, Crash models, Crash model validation, Interactive highway safety design model
TRT Terms: Traffic accidents–United States–Forecasting, Roads–United States–Interchanges and intersections–Mathematical models, Rural roads–United States, Low-volume roads–United States, signalized intersections