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Journal articles

Academic literature on the topic 'Passenger clusters'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Passenger clusters"

1

RACHAEL, T., K. SCHUBERT, W. HELLENBRAND, G. KRAUSE, and J. M. STUART. "Risk of transmitting meningococcal infection by transient contact on aircraft and other transport." Epidemiology and Infection 137, no. 8 (2009): 1057–61. http://dx.doi.org/10.1017/s0950268809002398.

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SUMMARYContact tracing of persons with meningococcal disease who have travelled on aeroplanes or other multi-passenger transport is not consistent between countries. We searched the literature for clusters of meningococcal disease linked by transient contact on the same plane, train, bus or boat. We found reports of two clusters in children on the same school bus and one in passengers on the same plane. Cases within each of these three clusters were due to strains that were genetically indistinguishable. In the aeroplane cluster the only link between the two cases was through a single travel e

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2

Cahigas, Maela Madel L., Ferani E. Zulvia, Ardvin Kester S. Ong, and Yogi Tri Prasetyo. "A Comprehensive Analysis of Clustering Public Utility Bus Passenger’s Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm." Sustainability 15, no. 9 (2023): 7410. http://dx.doi.org/10.3390/su15097410.

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Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers’ clusters. The alg

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3

Firdaus, Muhammad Iqbal, Reni Dian Octaviani, and Indri Yusnita. "CLASTERING CALON PENUMPANG KERETA CEPAT JAKARTA-BANDUNG." JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK 4, no. 2 (2017): 193. http://dx.doi.org/10.25292/j.mtl.v4i2.98.

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This research aims to cluster prospective passenger high speed rail service corridor Jakarta-Bandung to compensate the rapid development Bandung City as one of the favorite tourist destinations for domestic and international visitors. The data analysis Method is using non-hierarchical cluster and sampling technique by random sampling with 280 respondents. The results show that there are three clusters of prospective passenger for high speed rail service with different characteristics. The first clusters are those who depend heavily on their private vehicles, the second cluster which is the lar

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4

Firdaus, Muhammad Iqbal, Reni Dian Octaviani, and Indri Yusnita. "CLASTERING CALON PENUMPANG KERETA CEPAT JAKARTA-BANDUNG." Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) 4, no. 2 (2017): 193. http://dx.doi.org/10.54324/j.mtl.v4i2.98.

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This research aims to cluster prospective passenger high speed rail service corridor Jakarta-Bandung to compensate the rapid development Bandung City as one of the favorite tourist destinations for domestic and international visitors. The data analysis Method is using non-hierarchical cluster and sampling technique by random sampling with 280 respondents. The results show that there are three clusters of prospective passenger for high speed rail service with different characteristics. The first clusters are those who depend heavily on their private vehicles, the second cluster which is the lar

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5

Dell’Asin, Giulia, and Johannes Hool. "Pedestrian Patterns at Railway Platforms during Boarding: Evidence from a Case Study in Switzerland." Journal of Advanced Transportation 2018 (November 13, 2018): 1–11. http://dx.doi.org/10.1155/2018/4079230.

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The boarding/alighting process at railway platforms is an important determinant of the railway system performance and depends on the characteristics of passengers, the layout of the platform, and the rolling stock. This research aims to increase the understanding of the process, providing a methodological approach to model the passengers’ behaviour when boarding at railway platforms. Adequate criteria were selected to define the so called “boarding cluster” and an easy mechanism was developed to select the boarding clusters. Passenger flow data collected at Bern railway station in Switzerland

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6

Li, Xiaolu, Peng Zhang, and Guangyu Zhu. "DBSCAN Clustering Algorithms for Non-Uniform Density Data and Its Application in Urban Rail Passenger Aggregation Distribution." Energies 12, no. 19 (2019): 3722. http://dx.doi.org/10.3390/en12193722.

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With the emergence of all kinds of location services applications, massive location data are collected in real time. A hierarchical fast density clustering algorithm, DBSCAN(density based spatial clustering of applications with noise) algorithm based on Gauss mixture model, is proposed to detect clusters and noises of arbitrary shape in location data. First, the gaussian mixture model is used to fit the probability distribution of the dataset to determine different density levels; then, based on the DBSCAN algorithm, the subdatasets with different density levels are locally clustered, and at t

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7

Indah, Indri Cahaya, Mila Nirmala Sari, and Muhammad Halmi Dar. "Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu." SinkrOn 8, no. 2 (2023): 825–37. http://dx.doi.org/10.33395/sinkron.v8i2.12260.

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Transportation is an activity of moving things such as humans, animals, plants and goods from one place to another. To be able to implement transportation, we need a means of transportation that suits our needs. For in Indonesia, people are more inclined to land transportation. That's because land transportation already has a lot of vehicles. Land transportation already has many vehicles that can be used, both for private and for the public. Each vehicle has its uses and risks as well. Therefore we will do a data cluster from the trains. We chose the train, because the risk from using the trai

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8

Wu, Chaohua, and Xingzu Qi. "Short-term Bus Passenger Flow Forecast Based on CNN-BiLSTM." Advances in Engineering Technology Research 5, no. 1 (2023): 448. http://dx.doi.org/10.56028/aetr.5.1.448.2023.

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Effective prediction of urban bus passenger flow is critical for improving urban bus operation efficiency and optimizing the bus network. However, there are some issues with predicting urban bus passenger flow at the moment, such as lack of single eigenvalue consideration and insufficient research depth. In order to improve the short-term prediction accuracy of urban bus passenger flow, this paper proposed a deep learning prediction model that is based on CNN-BiLSTM. Based on historical data of urban bus passenger flow, this paper analyzes the dependence of bus credit card data, clusters the t

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9

Tang, Liyang, Yang Zhao, Kwok Leung Tsui, Yuxin He, and Liwei Pan. "A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data." Applied Sciences 10, no. 16 (2020): 5606. http://dx.doi.org/10.3390/app10165606.

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Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stabil

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10

Mariñas-Collado, Irene, Ana E. Sipols, M. Teresa Santos-Martín, and Elisa Frutos-Bernal. "Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models." Mathematics 10, no. 15 (2022): 2670. http://dx.doi.org/10.3390/math10152670.

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The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines

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