Pertanggungjawaban Pidana Terhadap Pencurian Barang Penumpang Pesawat Studi Dibandara Udara Kualanamu Internasional Airport

This study aims to determine that there are still many events that are still require the liability from the commercial air transportation company, both from the carrier company and those who are related to the carrier, such as flight delays (flight delay) either caused by weather factors or internal factors from the carrier company, the occurrence of negligence from the transport officer which causes the loss of goods owned by passengers, or because of there is an event for which the reason is unknown so that the aircraft experiences interference during the flight, from the results of this research it can be seen that the carrier operating the aircraft is obliged to be responsible for losses against:a. passengers who died, disability or injury;b. lost or damaged of the cabin baggage;c. lost, destroyed, or damaged of the checked baggage;d. lost, destroyed, or damaged of the cargo;e. delay in air transportation; andf. losses suffered by third partiesas well as who are the parties involved, the requirements that must be fulfilled and how the rights and the obligations of the parties are fulfilled, as well as other provisions in its implementation if a passenger's goods are lost or damaged and provide a description of its protection.Keywords : Liability, Theft of Goods, Aircraft Passengers, Kualanamu International Airport.

Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay Programs

Ground delay programs (GDPs) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may not result in optimal outcomes for passengers—especially with connecting itineraries. This paper proposes a novel passenger-centric optimization approach to GDPs by balancing flight and passenger delays in large-scale networks. For tractability, we decompose the problem using a rolling procedure, enabling the model’s implementation in manageable runtimes. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs through two main mechanisms: (i) delay allocation (delaying versus prioritizing flights) and (ii) delay introduction (holding flights to avoid passenger misconnections). In practice, however, passenger itineraries are unknown to air traffic managers; accordingly, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly. Results show that the proposed passenger-centric approach is highly robust to imperfect knowledge of passenger itineraries and can provide significant benefits even in the current decentralized environment based on collaborative decision making.

Quantile estimation of semiparametric model with time-varying coefficients for panel count data

Panel count data frequently occurs in follow-up studies, such as medical research, social sciences, reliability studies, and tumorigenicity experiences. This type data has been extensively studied by various statistical models with time-invariant regression coefficients. However, the assumption of invariant coefficients may be violated in some reality, and the temporal covariate effects would be of great interest in research studies. This motivates us to consider a more flexible time-varying coefficient model. For statistical inference of the unknown functions, the quantile regression approach based on the B-spline approximation is developed. Asymptotic results on the convergence of the estimators are provided. Some simulation studies are presented to assess the finite-sample performance of the estimators. Finally, two applications of bladder cancer data and US flight delay data are analyzed by the proposed method.