01559760

Component

Traffic microsimulation models contain many parameters which are randomized during the simulation process. The randomized parameters influence trip generation, vehicle characteristics, gap acceptance, and other aspects of simulated driver behavior, which ultimately affect output Measures of Effectiveness (MOEs) such as modeled traffic volumes and speeds. As part of a microsimulation model review process, it became necessary to investigate whether the statistical distribution of a specific model’s outputs matched the observed distributions for the study area. The sponsoring agency’s expectations (prior to the research) were that modeled and observed stochastiticties were broadly similar, and that individual model runs could be likened to traffic conditions on “different days.” Comparison of the statistical distributions of volume and speed data showed that field and model stochastiticties differed substantially: statistical dispersions in the microsimulation model were much narrower than the field observations. The modeled data roughly followed a normal distribution, but the field volume data was skew-distributed and the field speed data had a statistically multi-modal (double-hump) distribution. Traditional modeling practices often assume that the field data is normally distributed, which was certainly not the case in the congested urban freeway corridor used for this evaluation. The findings have implications for the model calibration process, the determination of the minimum number of simulation runs, and the interpretation of what comprises a “typical” set of traffic conditions in the model.

This paper was sponsored by TRB committee ADB50 Transportation Planning Applications.

01550057

15-5789

English

11p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Calibration; Data analysis; Dispersion (Statistics); Measures of effectiveness; Microsimulation; Stochastic processes; Traffic characteristics; Traffic data

Field data

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-5789

PRP, TRIS, TRB, ATRI

Dec 30 2014 1:56PM