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.

PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDAR UDARA INTERNASIONAL JUANDA MENGGUNAKAN METODE EXPONENTIAL SMOOTHING EVENT-BASED

The increase in the number of airplane passengers occurs at certain times, such as Eid al-Adha, Eid al-Fitr, and Christmas holidays. Of course, an excessive rise in the number of passengers can cause extreme flight traffic density so that which can cause flight delays, decreased airport service level performance, and other impacts. This study predicts the number of aircraft passengers at Juanda International Airport using the Exponential Smoothing Event-Based method. The Exponential Smoothing Event-Based method is a forecasting method that considers special events using the Exponential Smoothing method as the initial calculation. This study uses data on the number of passengers from January 2014 to December 2020. From the forecasting model, MAPE is 11.8905%, and MSE is 4202958561.0706, so that the resulting forecast can be categorized as good.

A Wind Tunnel Experimental Study of Icing on NACA0012 Aircraft Airfoil with Silicon Compounds Modified Polyurethane Coatings

Ice formation on the aerodynamic surfaces of an aircraft is regarded as a major problem in the aerospace industry. Ice accumulation may damage parts, sensors and controllers and alter the aerodynamics of the airplane, leading to a range of undesired consequences, including flight delays, emergency landings, damaged parts and increased energy consumption. There are various approaches to reducing ice accretion, one of them being the application of icephobic coatings. In this work, commercially available polyurethane-based coatings were modified and deposited on NACA 0012 aircraft airfoils. A hybrid modification of polyurethane (PUR) topcoats was adopted by the addition of nanosilica and three-functional spherosilicates (a variety of silsesqioxane compound), which owe their unique properties to the presence of three different groups. The ice accretion on the manufactured nanocomposites was determined in an icing wind tunnel. The tests were performed under three different icing conditions: glaze ice, rime ice and mixed ice. Furthermore, the surface topography and wetting behavior (static contact angle and contact angle hysteresis) were investigated. It was found that the anti-icing properties of polyurethane nanocomposite coatings strongly depend on the icing conditions under which they are tested. Moreover, the addition of nanosilica and spherosilicates enabled the reduction of accreted ice by 65% in comparison to the neat topcoat.

From data to complex network control of airline flight delays

Many critical complex systems and networks are continuously monitored, creating vast volumes of data describing their dynamics. To understand and optimize their performance, we need to discover and formalize their dynamics to enable their control. Here, we introduce a multidisciplinary framework using network science and control theory to accomplish these goals. We demonstrate its use on a meaningful example of a complex network of U.S. domestic passenger airlines aiming to control flight delays. Using the real data on such delays, we build a flight delay network for each airline. Analyzing these networks, we uncover and formalize their dynamics. We use this formalization to design the optimal control for the flight delay networks. The results of applying this control to the ground truth data on flight delays demonstrate the low costs of the optimal control and significant reduction of delay times, while the costs of the delays unabated by control are high. Thus, the introduced here framework benefits the passengers, the airline companies and the airports.

Causal Discovery of Flight Service Process Based on Event Sequence

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

Integrated Dataset of Brazilian Flights

The Brazilian commercial aviation system achieved the first position among Latin American countries and the fifteenth place worldwide on the Revenue Passenger-Kilometer ranking. The availability of flight information, including meteorological conditions, enables studies about the Brazilian flight system, such as flight delays and timetabling. Therefore, this paper contributes to such studies by offering an integrated dataset containing data on departure and arrival for flights departing and arriving at Brazilian airports comprising the period from 2000 to 2019. This paper presents a dataset composed of 15, 505, 922 records of flight data, each containing 45 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, scheduled and elapsed times for departure and arrival.