A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

Kim, Jiwon and Mahmassani, Hani S. (2014) A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times. Transportation Research Part C: Emerging Technologies, 46 83-97. doi:10.1016/j.trc.2014.05.011


Author Kim, Jiwon
Mahmassani, Hani S.
Title A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times
Journal name Transportation Research Part C: Emerging Technologies   Check publisher's open access policy
ISSN 0968-090X
1879-2359
Publication date 2014-09
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.trc.2014.05.011
Open Access Status
Volume 46
Start page 83
End page 97
Total pages 15
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Collection year 2015
Language eng
Abstract This study proposes an approach to modeling 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 Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) 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 Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma 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 the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.
Keyword Travel time reliability
Travel time variability
Finite mixture model
Gamma–Gamma distribution
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
Non HERDC
 
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
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Created: Mon, 04 Aug 2014, 12:54:42 EST by Jiwon Kim on behalf of School of Civil Engineering