A procedure was developed for using Surrogate Safety Assessment Model (SSAM) and VISSIM for safety assessment at freeway merge areas. The simulated conflicts generated by the VISSIM simulation models and identified by the SSAM approach to those measured in the field using traditional traffic conflicts techniques. Of particular interest was to identify if the consistency between the simulated and the field-measured traffic conflicts could be improved by calibrating the VISSIM simulation models and adjusting the threshold values in SSAM. A two-stage procedure was proposed to calibrate and validate the VISSIM simulation models. The transferability of the calibrated simulation models was also tested. It was found that the two-stage calibration procedure reduced the mean absolute prevent error (MAPE) for total conflicts from 78.1 to 33.4%. More specifically, the MAPE value was reduced from 76.6 to 33.5% for the rear-end conflicts, and from 79.5 to 35.8% for the lane-change conflicts. Linear regression models and the Spearman rank correlation coefficient were also developed to study the relationship between the simulated and the observed conflicts. Data analysis results showed that there was a reasonable consistency between the simulated and the observed conflicts.
Using VISSIM simulation model and Surrogate Safety Assessment Model for estimating field measured traffic conflicts at freeway merge areas
IET Intelligent Transport Systems ; 7 , 1 ; 68-77
2013-03-01
10 pages
Article (Journal)
Electronic Resource
English
SSAM approach , lane-change conflicts , road safety , freeway merge areas , linear regression models , surrogate safety assessment model , traffic conflicts techniques , goodness-of-fit , data analysis , field measured traffic conflict estimation , mean absolute prevent error , rear-end conflicts , two-stage calibration procedure , road traffic , VISSIM simulation model , traffic engineering computing , digital simulation , MAPE , Spearman rank correlation coefficient , regression analysis
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