title: Calibration and validation of simulated vehicle safety performance at signalized intersections.
abstract: A systematic procedure is presented for calibrating and validating a microscopic model of safety performance. The context in the model application is the potential for rear-end crashes at signalized intersections. VISSIM v.4.3 provides the simulation platform for estimating the safety performance for individual vehicles and has been calibrated and validated using separate samples of observed vehicle tracking data extracted from the FHWA/NGSIM program. The calibration exercise involves four sequential steps: (1) heuristic selection of initial model inputs, (2) statistical screening using a Plackett-Burnman design, (3) fractional factorial analysis relating inputs to safety performance, and (4) genetic algorithm procedure for obtaining best estimate input values. Three measures of safety performance were considered: crash potential index, number of vehicles in conflict and total conflict duration per vehicle. Model consistency was assessed by comparing simulated and observed safety performance based on a separate validation sample of vehicle tracking data. The suggested procedure was found to effectively estimate model input parameters that closely matched safety performance measures in the observed validation data. This procedure yields an objective and efficient means for simulation model calibration applied for estimating safety performance at signalized intersections.
authors.affiliation: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Street West, N2L 3G1 Waterloo, ON, Canada. fjccunto@uwaterloo.ca
authors.forename: Flávio
authors.initials: F
authors.lastname: Cunto
authors.forename: Frank F
authors.initials: FF
authors.lastname: Saccomanno
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D002138:Calibration; D003198:Computer Simulation / Q000706:statistics & numerical data*; D006801:Humans; D008853:Microscopy; D015233:Models, Statistical; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D009864:Ontario; D012449:Safety / Q000706:statistics & numerical data*