z-logo
open-access-imgOpen Access
Modeling distributions of travel time variability for bus operations
Author(s) -
Ma Zhenliang,
Ferreira Luis,
Mesbah Mahmoud,
Zhu Sicong
Publication year - 2016
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.1314
Subject(s) - computer science , reliability (semiconductor) , normality , robustness (evolution) , travel time , statistics , econometrics , simulation , transport engineering , power (physics) , engineering , mathematics , physics , biochemistry , chemistry , quantum mechanics , gene
Summary Bus travel time reliability performance influences service attractiveness, operating costs, and system efficiency. Better understanding of the distribution of travel time variability is a prerequisite for reliability analysis. A wide array of empirical studies has been conducted to model distribution of travel times in transport. However, depending on the data tested and approaches applied to examine the fitting performance, different conclusions have been reported. This paper aims to specify the most appropriate distribution model for the day‐to‐day travel time variability by using a novel evaluation approach and set of performance measures. Two important issues are explored using automatic vehicle location data collected on two typical bus routes over 6 months in Brisbane, namely, data aggregation influences on travel time distribution and comprehensive evaluation of performance of distribution models. The decrease of temporal aggregation of travel times tends to increase the normality of distributions. The spatial aggregation of link travel times would break up the link multimodality distributions for a busway route, but unlike for a non‐busway route. The Gaussian mixture models are evaluated as superior to its alternatives in terms of fitting accuracy, robustness, and explanatory power. The reported distribution model shows promise to fit travel times for other services with different operation environments considering its flexibility in fitting symmetric, asymmetric, and multimodal distributions. The improved statistic fitting can support more effective service reliability analysis. Copyright © 2015 John Wiley & Sons, Ltd.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here