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Robust Calibration of Macroscopic Traffic Simulation Models Using Stochastic Collocation
Transportation Research Procedia
The predictions of a well-calibrated traffic simulation model are much more valid if made for various conditions. Variation in traffic can arise due to many factors such as time of day, work zones, weather, etc. Calibration of traffic simulation models for traffic conditions requires larger datasets to capture the stochasticity in traffic conditions. In this study we use datasets spanning large time periods to incorporate variability in traffic flow, speed for various time periods. However,doi:10.1016/j.trpro.2015.07.001 fatcat:ip3c7a6uxzcmxjzr2owodmg3gq