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- W1496925769 abstract "This study proposes an approach to analyze the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma-Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of GG models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation-Maximization algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component GG mixture model is specified. The distribution fitting results show that the mixture model provides better fits to travel delay observations than the standard (one-component) GG model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing a better understanding of the mechanisms causing travel time unreliability in a traffic network." @default.
- W1496925769 created "2016-06-24" @default.
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- W1496925769 date "2014-01-01" @default.
- W1496925769 modified "2023-09-26" @default.
- W1496925769 title "A Finite Mixture Model of Vehicle-to-Vehicle and Day-to-Day Variability of Traffic Network Travel Times" @default.
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- W1496925769 hasPublicationYear "2014" @default.
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