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2 2024

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Data investigation of PK-LQP airplane vertical deviation using class 1 and tier 1 datasets

Passarella Rossi, Nurmaini Siti, Rachmatullah Muhammad Naufal, Veny Harumi, Ubaya Huda, Firdaus Firdaus

Zeszyty Naukowe. Transport / Politechnika Śląska

2024

z. 124

127--142

The vertical deviation is one of the important stages in commercial aircraft flights because indications of malfunction can be detected faster by the pilot rather than in other flight phases. The purpose of this study is to investigate whether the PK-LQP airplane experienced unusual altitude movements during the vertical deviation phase since the airplane taking off from Seattle (Boeing Manufacture's home base) until the airplane crash. The airplane, which is of the type B737 MAX8, had operated for 83 days, during which it completed 438 flights using a total of 107 flight codes, and it travelled to 36 airports. According to the findings of an investigation of the data, we found that only 69 (39%) were included in tier 1, which had an ADS-B data update interval message below 10 seconds, complying with ICAO and FAA-AVN standards. With this class 1 and tier 1 dataset, we conducted an EDA to find the data insight, which revealed that there was a disturbance in the speed and altitude indications on the airplane instrumentation, causing a misperception for the pilot and causing the airplane to drastically drop in altitude (more than 100 feet).

Using Machine Learning to Identify Clustering Patterns of Traffic Accidents

Passarella Rossi, Utami Putri Shobiroh, Ubaya Huda, Widyastuti Rifka

Prace Naukowe Politechniki Warszawskiej. Transport

2024

z. 139

19--32

Between 20 and 50 million people suffer non-fatal injuries from traffic accidents each year, while more than one million of these result in death. Road traffic accidents pose a significant threat not only to the economy but also to public health. The figures obtained are objective statistics and not based on personal opinions. This study aims to develop a clustering model using a machine learning approach based on the characteristics of the occurrence and number of victims of traffic accidents in Palembang City, South Sumatra Province, Indonesia. This research will analyze patterns that observe different relationships among crashes by using various clustering techniques such as K-means clustering, Gaussian mixture model (GMM), density-based (DBSCAN), hierarchical clustering, spectral clustering, and OPTICS (sorted points to identify clustering structure). Through the machine learning approach, this research attempts to bridge the knowledge gap between the factors involved in traffic accidents and the development of more effective prevention strategies. In addition, this research also provides further insight into the potential use of machine learning algorithms in analyzing and processing traffic accident data. The results show that an algorithm called spectral clustering outperforms the other algorithms with a low Davies-Bouldin score (0.3221), a high Calinski-Harabasz score (14789.9374), and a silhouette coefficient (0.7695). Spectral clustering was the best algorithm out of the six algorithms evaluated for this paper. By favoring spectral clustering as the best algorithm, this research provides a new outlook on the application of technology in the field of highway safety. The results also show that there are specific patterns of traffic accidents in Palembang City that can be identified through data analysis using clustering techniques. The implications of this research provide an important contribution to the development of strategies for traffic accident prevention and road safety improvement in the region. It is expected that the findings can assist the government and related agencies in taking more effective measures to reduce the number of traffic accidents and protect the public from the associated risks.