Abstract
A better understanding of the travel time distribution shape or pattern could improve the decision made by the transport operator to estimate the travel time required for the vehicle to travel from one place to another. Finding the most appropriate distribution to represent the day-to-day travel time variation of an individual link of a bus route is the main purpose of this study. Klang Valley, Malaysia is the study area for the research. A consecutive of 7 months ten bus routes automatic vehicle location (AVL) data are used to examine the distribution performance. The leading distribution proposed for the research is the Burr distribution. Both symmetrical and asymmetrical distributions that have been proposed in existing studies are also used for comparison purposes. Maximum likelihood estimation is applied for parameter estimation while loglikelihood value, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are applied for performance assessment of the distributions. Promising results are obtained by the leading model in all different kinds of operating environment and could be treated as the preliminary preparation for further reliability analysis.
Highlights
Travel time variability (TTV) has always been recognized as the key indicator to evaluate the service quality of the transportation system
The focus of this study is in finding the most appropriate model to explain each section of weekday and weekend travel time values separately for day-to-day TTV analysis
The Burr distribution was proposed as the leading model while several candidature models, i.e. normal, lognormal, gamma, Weibull and generalized Pareto were used for comparison purposes
Summary
Travel time variability (TTV) has always been recognized as the key indicator to evaluate the service quality of the transportation system. It means punctuality or the consistency of the transportation system for a certain journey. In order to improve the attractiveness of the public transportation system, it is important to maximize its service quality or reliability and improve the performance of on-time arrival. This is always a challenge for the road transport operator as the traffic systems have a stochastic nature.
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