Passenger Baggage Research Articles

Analisis Penanganan Kehilangan Bagasi Penumpang dan Bagasi Tidak Bertuan Garuda Indonesia Oleh Ground Handling PT Gapura Angkasa di Bandar Udara Juanda Surabaya

Garuda Indonesia collaborates with companies engaged in Ground Handling to handle problems faced by passengers including baggage problems. This research aims to identify and explain the system for handling lost passenger baggage and unclaimed baggage by Garuda Indonesia and how to reduce it by Ground Handling PT Gapura Angkasa. The method in this research is descriptive qualitative by means of structured interviews with baggage service unit officers and make observation at the baggage service unit office at Juanda Airport Surabaya on 12 August-23 August 2021 and documentation of field work. In analyzing the data, the researcher went through several stages,that is data reduction, data presentation, and conclusions. The results of the data analysis that the researcher has carried out show that the baggage loss handling system, the officer will search for 14 days from the reporting date and the baggage handling officer will identify the ownership of the baggage. The responsibility of PT. Garuda Indonesia is guided by PM Number 77 of 2011 and on WI (Work Instructions) to reduce the occurrence of baggage loss and unclaimed baggage, namely, passengers are advised to provide a special/special identity on the baggage, check-in counters always provide baggage tag stock, make-up area must always be bright so as to reduce the occurrence of baggage irregularities Passengers always remember their luggage.
 Keywords: Garuda Indonesia, Baggage Service Unit, Baggage Handling Procedures, Missing Baggage, On Hand Baggage.

The Bugs in the Bags: The Risk Associated with the Introduction of Small Quantities of Fruit and Plants by Airline Passengers.

Simple SummaryThis study was carried out with the aim of emphasizing the importance of checking the plant material that can be imported in the baggage of airline passengers. Travelers are often unaware of the regulations in place and of the risks connected with such importation. The risk of the introduction of harmful organisms correlated with this pathway is yet not well studied and its frequency is underestimated. The results of the research underline the need for continuous checks at entry points and the establishment of a specialized position for inspections.Among European countries, Italy is the most exposed to the risk of biological invasions, principally for its numerous entry points (ports and airports) and for climatic conditions favorable for the acclimatization of several invasive species. Here it was assessed that the greatest threats to our agro-ecosystems come mainly from the passenger baggage in which a variety of fruits and vegetables are carried. From 2016 to 2021, large quantities of plant products were found in the luggage of passengers travelling from outside the EU and seized at the BCPs (border control posts) in the Campania region. Inspections and the following laboratory analyses were conducted on the plant material to assess the presence of exotic pests. Inspections led to several non-native species being recorded, and among the intercepted organisms, some should be considered “alarming”, such as Bactrocera dorsalis, Anastrepha obliqua, and Leucinodes africensis. Despite a well-organized border inspection system, travelers transporting infested material unknowingly contribute to increasing the risk of the introduction of exotic species. Given the current situation, it is necessary to impose stricter controls and greater attention, ensuring compliance with the requirements of the new phytosanitary regulations by the actors involved in the transport of plant material. Finally, it is essential to improve awareness through a phytosanitary campaign on plant health risks, especially for people wishing to transport fruits and vegetables in their luggage.

A discussion on design fires for high-speed railway train cars

High-speed rail systems are expanding everywhere for carrying thousands of passen-gers every day. Train cars in underground spaces travel under dense urban areas, tun-nels in mountainous places and bridges across rivers. Design fire in burning a train car should be studied carefully for fire hazard assessment in those long underground or en-closed spaces. There are full-scale burning testing results on the main combustibles in-cluding seat, curtain and passenger luggage in some train cars. The heat release rate of a high-speed train car model CRH1 will be studied using such data together with a two-layer fire zone model, the Consolidated Model of Fire and Smoke Transport (CFAST). Simulation will be performed on the full-sized train car with the heat release rate of the seat measured by experiment. Two fire scenarios with ignition points located in the front and rear areas of the train compartment are considered. The results of the fire simulations show that the heat release rate can reach 43 MW. The value is similar to the full-scale burning experiments on train cars in Sweden and South Korea. Active fire protection systems have to be seriously considered in those underground or enclosed areas.

Designing Dynamic Inductive Charging Infrastructures for Airport Aprons with Multiple Vehicle Types

In the effort to combat climate change, the CO2 emissions of the aviation sector must be reduced. The traffic caused by numerous types of ground vehicles on airport aprons currently contributes to those emissions as the vehicles typically operate with combustion engines, which is why an electrification of those vehicles has already begun. While stationary conductive charging of the vehicles is the current standard technology, dynamic wireless charging might be an attractive technological alternative, in particular for airport aprons; however, designing a charging network for an airport apron is a challenging task with important technical and economic aspects. In this paper, we propose a model to characterize the problem, especially for cases of multiple types of vehicles sharing the same charging network, such as passenger buses and baggage vehicles. In a numerical study inspired by real-world airports, we design such charging networks subject to service level constraints and evaluate the resulting structures via a discrete-event simulation, and thus, show the way to assess the margin of safety with respect to the vehicle batteries’ state of charge that is induced by the spatial structure of the charging network.

ANALISIS PENGAWASAN PETUGAS AVIATION SECURITY (AVSEC) DALAM MENDETEKSI BAHAN PELEDAK DI BANDAR UDARA KOMODO LABUAN BAJO

Aviation Security (AVSEC) officer still frequently discover dangerous goods in passenger luggage, such as scissors, knives, small gas cylinders, machetes/swords, razors, gas lighters, cutlery, a power bank that exceeds the limit, sea shells, pink sand, and marine life in the Prohibited Item category. Researchers also discovered that many passengers were bringing alcoholic beverages that were not labeled and exceeded the specified standards. This research aims 1) To discover how Aviation Security (AVSEC) officers play a part to aviation security and safety at Komodo Labuan Bajo Airport. 2) To determine what equipment is required to support Security Check Point (SCP) two in maintaining aviation security and safety at Komodo Labuan Bajo Airport. 3) To find out how Komodo Labuan Bajo Airport Aviation Security (AVSEC) officers handle dangerous explosive materials. This research uses qualitative methods. Data collection is carried out by observation methods, interviews, and documentation which are then carried out data analysis using data reduction, data presentation and conclusions. The results of this study reveal that 1) Aviation Security Officers (AVSEC) have a very important role in ensuring flight security and safety, 2) Passenger inspections on SCP 2 are carried out Security checks on SCP 2 must be supported by inspections with flight security aids, where inspections This is done by using X-Ray machine tools, Walk Through Metal Detector (WTMD), and Hand Hel Metal Detector (HHMD), 3) Aviation Security Officers (AVSEC) in handling explosives that are considered dangerous to all parties must carry out regular inspections. strict restrictions on passengers and goods that enter the area at Komodo Labuan Bajo Airport to prevent the transportation of prohibited and dangerous goods and can be used to threaten safety and security.

LEGAL PROTECTION FOR CONSUMERS IN THE CASE OF EXCESS SPEED BOAT CAPACITY AT DUFA PORT – DUFA

In a carriage agreement, the position of the parties, namely the carrier and the service user, is equal. As for the things that can cause harm to passengers due to the fault of the carrier, among others, accidents caused by the negligence of the captain, conditions of transportation that are not suitable for use, or due to lost or damaged passenger luggage. The government should increase the socialization activities of Law no. 17 of 2008, both to sea transportation operators and to the wider community as users of sea transportation services, so that legal protection efforts against sea transportation service users (passengers) as regulated in Law no. 17 of 2008 can actually be implemented by all sea transportation companies.

How Realistic Is Threat Image Projection for X-ray Baggage Screening?

At airports, security officers (screeners) inspect X-ray images of passenger baggage in order to prevent threat items (bombs, guns, knives, etc.) from being brought onto an aircraft. Because threat items rarely occur, many airports use a threat-image-projection (TIP) system, which projects pre-recorded X-ray images of threat items onto some of the X-ray baggage images in order to improve the threat detection of screeners. TIP is regulatorily mandated in many countries and is also used to identify officers with insufficient threat-detection performance. However, TIP images sometimes look unrealistic because of artifacts and unrealistic scenarios, which could reduce the efficacy of TIP. Screeners rated a representative sample of TIP images regarding artifacts identified in a pre-study. We also evaluated whether specific image characteristics affect the occurrence rate of artifacts. 24% of the TIP images were rated to display artifacts and 26% to depict unrealistic scenarios, with 34% showing at least one of the two. With two-thirds of the TIP images having been perceived as realistic, we argue that TIP still serves its purpose, but artifacts and unrealistic scenarios should be reduced. Recommendations on how to improve the efficacy of TIP by considering image characteristics are provided.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network.

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

Multi-Energy Gamma-Ray Attenuations for Non-Destructive Detection of Hazardous Materials

We present a non-destructive method (NDM) to identify minute quantities of high atomic number (Z) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources 133Ba (half-life = 10.55 y) and 152Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.

Worldwide border interceptions provide a window into human-mediated global insect movement.

As part of national biosecurity programs, cargo imports, passenger baggage, and international mail are inspected at ports of entry to verify compliance with phytosanitary regulations and to intercept potentially damaging nonnative species to prevent their introduction. Detection of organisms during inspections may also provide crucial information about the species composition and relative arrival rates in invasion pathways that can inform the implementation of other biosecurity practices such as quarantines and surveillance. In most regions, insects are the main taxonomic group encountered during inspections. We gathered insect interception data from nine world regions collected from 1995 to 2019 to compare the composition of species arriving at ports in these regions. Collectively, 8,716 insect species were intercepted in these regions over the last 25yr, with the combined international data set comprising 1,899,573 interception events, of which 863,972 were identified to species level. Rarefaction analysis indicated that interceptions comprise only a small fraction of species present in invasion pathways. Despite differences in inspection methodologies, as well as differences in the composition of import source regions and imported commodities, we found strong positive correlations in species interception frequencies between regions, particularly within the Hemiptera and Thysanoptera. There were also significant differences in species frequencies among insects intercepted in different regions. Nevertheless, integrating interception data among multiple regions would be valuable for estimating invasion risks for insect species with high likelihoods of introduction as well as for identifying rare but potentially damaging species.

Measuring the Efficiency of Automation-Aided Performance in a Simulated Baggage Screening Task.

The present study replicated and extended prior findings of suboptimal automation use in a signal detection task, benchmarking automation-aided performance to the predictions of several statistical models of collaborative decision making. Though automated decision aids can assist human operators to perform complex tasks, operators often use the aids suboptimally, achieving performance lower than statistically ideal. Participants performed a simulated security screening task requiring them to judge whether a target (a knife) was present or absent in a series of colored X-ray images of passenger baggage. They completed the task both with and without assistance from a 93%-reliable automated decision aid that provided a binary text diagnosis. A series of three experiments varied task characteristics including the timing of the aid's judgment relative to the raw stimuli, target certainty, and target prevalence. Automation-aided performance fell closest to the predictions of the most suboptimal model under consideration, one which assumes the participant defers to the aid's diagnosis with a probability of 50%. Performance was similar across experiments. Results suggest that human operators' performance when undertaking a naturalistic search task falls far short of optimal and far lower than prior findings using an abstract signal detection task.

A Qualitative Study on Providing Alternative Solutions for Handling the HSR Passenger\u2019s Luggage

The objective of this paper is to look at the problem of high-speed rail (HSR) passengers’ luggage and provide alternative solutions to improve luggage handling. A qualitative study is carried out to assess the current state of HSR practices, analyse existing issues with HSR passenger luggage and provide alternative solutions, including modification of the passenger unit, double-deck rolling stock, an additional train for transporting luggage, and repositioning of passenger seats. Four solutions are discussed that could solve the problem of HSR passenger luggage handling and evaluated from four aspects namely passengers, railway operator, cost and passenger comfort level. The solution of additional trains for transporting luggage may be the preferred solution when compared with other options, without compromising the comfort of the passengers or the profit of the railway company.

A Searching Space Constrained Partial to Full Registration Approach With Applications in Airport Trolley Deployment Robot

For airports with high passenger and luggage flows, a large number of staff members have to be hired to deploy the scattered passenger luggage trolleys. To release humans from the repetitive and laborious job, we develop an autonomous trolley deployment robot to detect, transport and collect the scattered idle trolleys to recycling points. This paper will firstly illustrate the entire collection pipeline of the deployment robot system and then address the key challenge: partial to full point set registration. With the perception framework, the robot can detect the idle trolleys and acquire the pose of the trolleys on the ground, and then capture the trolley from behind, along the same direction for subsequent grasping and manipulation. With RGB-D camera and a segmentation Convolutional Neural Network, the robot can generate a partial surface point cloud of the detected trolley. The resulting point cloud, data and a pre-scanned full trolley point cloud, model, are matched by an implicit pose. To tackle the low accuracy and long computation time issues, a novel searching space-constrained point set registration algorithm is proposed to register the two overlapping point sets. Based on Branch-and-Bound (BnB) mechanism, the error between data and model is iteratively optimized. The constraint of searching space speeds up the global searching of the optimal pose, by pruning the candidate spaces which is impossible to contain the optimal result. To evaluate the performance, an airport trolley segmentation dataset and a point cloud dataset for registration are constructed. Experimental results on the datasets and synthetic dataset show that our method achieves higher accuracy and success rate than the previous methods. The experiments demonstrated in video clips validate the developed system works in real-world applications.

Detection of threat objects in baggage inspection with X-ray images using deep learning

In the field of security, baggage-screening with X-rays is used as nondestructive testing for threat object detection. This is a common protocol when inspecting passenger baggage particularly at airports. Unfortunately, the accuracy of such human inspection is around 80–90%, under optimal operator conditions. For this reason, it is quite necessary to assist human inspectors with the aid of computer vision algorithms. This work proposes a deep learning-based methodology designed to detect threat objects in (single spectrum) X-ray baggage scan images. For this purpose, our proposed framework simulates a large number of X-ray images, using a combination of PGGAN (Karras et al. in International conference on learning representations, 2018. https://openreview.net/forum?id=Hk99zCeAb ) and superimposition (Mery and Katsaggelos in 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), 2017. https://doi.org/10.1109/CVPRW.2017.37 ) strategies, that are used to train state-of-the-art detection models such as YOLO (Redmon et al. in You only look once: unified, real-time object detection. CoRR abs/1506.02640, 2015. http://arxiv.org/ abs/1506.02640 ), SSD (Liu et al. in SSD: single shot multibox detector. CoRR abs/1512.02325, 2015. http://arxiv. org/abs/1512.02325 ) and RetinaNet (Lin et al. in Focal loss for dense object detection. CoRR abs/1708.02002, 2017. http://arxiv.org/abs/1708.02002 ). Our method has been tested on real X-ray images in the detection of four categories of threat objects: guns, knives, razor blades and shuriken (ninja stars). In our experiments, YOLOv3 (Redmon and Farhadi in Yolov3: An incremental improvement. CoRR abs/1804.02767, 2018. http://arxiv.org/abs/1804.02767 ) obtained the best mean average precision (mAP) with 96.3% for guns, 76.2% for knives, 86.9% for razor blades and 93.7% for shuriken, while the average mAP for all threat objects was 80.0%. We believe the effectiveness of our method in the detection of threat objects makes its use in checkpoints possible. Moreover, our methodology is scalable and can be easily extended to detect other categories automatically.

A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of detecting the presence of threats from the scanned images yet requires the involvement of human in determining what type of threat and where it is located. Deep learning-based object detection technique has not been successfully implemented to detect threats in the security screening processes. This research aims to improve the accuracy and the processing time of threat detection in security screening. Enhanced faster region-based convolutional neural network (faster R-CNN) with improved region proposals is used for better threat localization. The proposed system consists of an improved region proposal network that outputs object’s region proposals with an object score to the detector module to accurately locate the threat in the human body. Furthermore, this system uses a modified loss function that strengthens the classification loss. Results obtained by the proposed model show a 15% improvement in object localization. Therefore, the enhanced faster R-CNN achieves an overall detection accuracy of 0.27 in terms of average precision and reduces the processing time by 0.19 s. The results obtained by the enhanced faster R-CNN for detection accuracy are superior to the state-of-the-art system. Also, this model focuses on localizing the threat and identifying its type, which makes the model suitable for threat detection security screening. Besides, the research also addresses the time consumption issue in detecting the threat object.

Fuzzy control in the simulation model of airport baggage handling systems

The object of the research is the airport baggage handling system with a significant share of transfer passengers. It is proposed to include into the composition of simulation models used in solving problems of efficiency analysis and design of baggage handling systems, a fuzzy algorithm that simulates the strategy of a human operator who controls the process of pre-flight passenger service and baggage handling in the airport terminal. In practice, management ensures the economy of the airport resources, the ability to maneuver personnel and technical means in order to reduce the unproductive time spent on ground handling and improve its quality. An algorithm of fuzzy inference on a singleton rule base is described, which can be considered as a special case of the Mamdani algorithm. The results of solving problems of computer statistical modeling using a fuzzy control algorithm are presented, indicating the need to take into account the control of ground processes to increase the adequacy and accuracy of simulation models of baggage handling systems.

Migration, Emigration, and Immigration: African Cartoonists Draw the Lines

Migration, Emigration, and Immigration: African Cartoonists Draw the Lines

An Automatic Disinfection System for Passenger Luggage at Airports and Train/Bus Stations.

Many researchers are working on multiple aspects of the COVID-19 pandemic including disease detection, treatment, and vaccine development. It is expected that there will be a large increase in passenger traffic at airports and railway stations and other public places. This paper proposes one solution to reduce the transmission of the disease through fomites such as passenger luggage and packages at bus/train stations and airports in the country. A tunnel system similar to the X-ray machines used for passenger luggage at airports has been proposed for disinfection of fomites. The system consists of eight 36 W T8 TUV bulbs illuminating each square meter area of the fomites on a conveyor belt for 10 s. For the standard airline luggage dimensions, 24 such bulbs will be distributed evenly on all four sides of the tunnel. The entry and exit points on the conveyor are shielded from the UV-C light leakage by placing thin plastic (such as acrylic) curtains. An optional non-foaming soap solution spray system may be used as an additional disinfection step. The toxic sodium hypochlorite solutions are not used in this design. Non-foaming soap solutions which are very effective against coronavirus and are non-toxic and biodegradable may be used as an optional disinfectant. It is to be noted that while the disinfection systems are designed to effectively mitigate the threat of coronavirus on the passenger fomite surfaces, these are purely based on theoretical calculations and have not been tested with actual viral particles. However, considering the time-sensitive emergency with COVID-19 pandemic, these systems will be very useful even without rigorous campaign of testing.

Automatic x-ray image analysis for aviation security within limited computing resources

The objective of this work is to develop approaches to automating inspection procedure at airports. The article presents the deficiencies of the existing inspection system, concluding in the negative impact of the human factor. It is proposed to use convolutional neural networks for automatic x-ray image analysis of passenger baggage. The paper presents the results of the convolutional neural network with various input data and architecture within limited computing resources. In a view to further development, this study can contribute to the development of specialized software to help aviation security screeners through partial automation of their work.

EKSPOR DAN IMPOR BARANG SERTA PERPAJAKAN BAGI PELAKU USAHA UMKM

The object of this community service activity is the Micro-Small Business, Banana Chips Karya Mandiri. The purpose of this community service activity is to provide a basic understanding and foster awareness of business people, especially the object of the Banana Karya Karya Mandiri, in fulfilling tax obligations and providing information related to the export or import of goods that can be carried out in connection with the business being run. These community service activities are divided into two main sub-categories namely taxation and export / import of goods. For sub-taxation activities carried out in the form of an explanation related to tax obligations that must be carried out by the SME business include registration as a taxpayer, calculation of the amount of tax owed (as currently regulated in PP 23), depositing the tax owed to the state treasury and reporting annual tax return, provides an overview of the role of taxes generated from the SME business in the realization of state revenue from the taxation sector year on year, as well as the importance of taxes for the country's development. As for the sub-export / import of goods, the activities carried out are the socialization of Import and / or Export of Personal Goods and Goods for Business Entities, passenger luggage and shipments from and abroad, as well as material on hajj luggage. The results of this service are providing knowledge / education about taxation for MSMEs as well as providing knowledge / education regarding the provisions on the import and / or export of personal goods and goods for business entities as well as the provisions for hajj and umrah luggage.