Abstract
The study aims to model the distribution of motorcycle departures from the parking lot at peak times with the Poisson process approach. This process involves a discrete number of departures and continuous interdeparture time. The probability distribution candidate was selected to model the data according to the data nature, the stochastic process, and the empirical observation of the departure process. Parameters are estimated using the Maximum Likelihood Estimation (MLE) method and the bootstrapping procedure to construct confidence intervals for the parameter. The goodness-of-fit test is applied to select the best probability distribution that matches empirical data. Inferences to the distribution parameters suggest that Weibull's distribution is more appropriate for describing the motorcycle's inter-departure time. The number of motorcycle departures fits significantly into a negative binomial distribution. The results of the study concluded that the Poisson process applied was a case of overdispersion, with the motorcycle departure rate decreasing over time.Keywords: Bootstrapping, departure, distribution, goodness-of-fit, PoissonMSC2020: 60E05
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
