DocumentCode
3568329
Title
Parameter sampling strategies in traffic microsimulation
Author
Punzo, Vincenzo ; Montanino, Marcello
Author_Institution
Dept. of Civil, Environ. & Archit. Eng., Univ. of Naples Federico II, Naples, Italy
fYear
2015
Firstpage
45
Lastpage
51
Abstract
The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results revealed that model performances in case of sampling from normal pdfs are extremely poor. In turn, results proved that parameter correlation, as well as the parameter distribution model, entail a big impact on model performances and should be properly take into account in the microsimulation practice.
Keywords
digital simulation; normal distribution; road traffic; traffic engineering computing; car-following model parameter; independent normal distributions; lane-changing model parameter; parameter correlation; parameter distribution model; parameter sampling strategies; sampling parameters; simulation software; traffic microsimulation; Calibration; Correlation; Data models; Software; Stochastic processes; Trajectory; Vehicles; calibration; driver heterogeneity; parameter sampling; traffic micro-simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on
Print_ISBN
978-9-6331-3140-4
Type
conf
DOI
10.1109/MTITS.2015.7223235
Filename
7223235
Link To Document