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 :
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