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Journal articles on the topic 'Passenger clusters'
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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 (March 19, 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 episode. The onset of illness (2 and 5 days after the flight) is consistent with infection from an unidentified carrier around the time of air travel. In contrast to the established risk of transmission from a case of tuberculosis, it is likely that the risk from a case of meningococcal disease to someone who is not identified as a close contact is exceedingly low. This should be considered in making international recommendations for passenger contact tracing after a case of meningococcal disease on a plane or other multi-passenger transport.
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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 (April 29, 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 algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.
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Firdaus, Muhammad Iqbal, Reni Dian Octaviani, and Indri Yusnita. "CLASTERING CALON PENUMPANG KERETA CEPAT JAKARTA-BANDUNG." JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK 4, no. 2 (September 11, 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 largest cluster has logical ridership characteristics and the last cluster is a group that has a high level of concern for the environment.
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Firdaus, Muhammad Iqbal, Reni Dian Octaviani, and Indri Yusnita. "CLASTERING CALON PENUMPANG KERETA CEPAT JAKARTA-BANDUNG." Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) 4, no. 2 (July 7, 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 largest cluster has logical ridership characteristics and the last cluster is a group that has a high level of concern for the environment.
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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 was used to test the proposed approach. The results show that (a) the clusters near the doors grow in the longitudinal direction with a rate of 6:1 between the length and width of clusters, and that (b) the growth curves rise quickly when clusters are still small, i.e., at the beginning of the boarding/alighting activity. Further research is needed to extend the validation of the model, considering other variables, such as critical pedestrian densities which occur at specific hot spots near obstacles at platforms.
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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 (September 29, 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 the same time, the appropriate seeds are selected to complete the cluster expansion; finally, the subdatasets clustering results are merged. The method validates the clustering effect of the proposed algorithm in terms of clustering accuracy, different noise intensity and time efficiency on the test data of public data sets. The experimental results show that the clustering effect of the proposed algorithm is better than traditional DBSCAN. In addition, the passenger flow data of the night peak period of the actual site is used to identify the uneven distribution of passengers in the station. The result of passenger cluster identification is beneficial to the optimization of service facilities, passenger organization and guidance, abnormal passenger flow evacuation.
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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 (April 4, 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 train is very small, meaning that there is a lot of public interest in trains. So we want to do a cluster on rail passengers. The cluster that we do is to group passenger data based on the similarity of passenger data. We will do the cluster using the K-Means method. The K-Means method is very suitable when used to perform a cluster. K-Means will process widgets that are made according to the needs of the research. So after we enter the method in the widget pattern, the widget will process it to output the results from the cluster that we created. The cluster process using the K-Means method will be applied using the orange application. After we apply it, the data will later be clustered, we will cluster data as many as 3 clusters. Then the incoming data will appear in clusters 1, 2 and 3, both from business and executive classes
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Wu, Chaohua, and Xingzu Qi. "Short-term Bus Passenger Flow Forecast Based on CNN-BiLSTM." Advances in Engineering Technology Research 5, no. 1 (May 19, 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 travel feature of different groups of people, and analyzes the dependence of bus passengers. Simultaneously, external factors of passenger flow, such as rainfall, weather condition, traffic flow state, and date, are introduced to build the bus passenger flow prediction feature matrix, and the correlation analysis of the characteristic matrix structure is performed to optimize the matrix structure. Finally, the optimized passenger flow characteristic matrix is fed into the CNN-BiLSTM deep learning model for prediction, and the results are compared to the LSTM, CNN and CNN-LSTM models. The results shown that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the the CNN-BiLSTM deep learning model are lower than those of other models, and the prediction accuracy is the highest. Meanwhile, this method has a good generalization effect and can improve deep learning prediction accuracy.
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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 (August 13, 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 stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.
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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 (July 28, 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 or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem.
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Gu, Donglian, Yixing Wang, Xinzheng Lu, and Zhen Xu. "Probability-Based City-Scale Risk Assessment of Passengers Trapped in Elevators under Earthquakes." Sustainability 15, no. 6 (March 8, 2023): 4829. http://dx.doi.org/10.3390/su15064829.
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An accurate prediction of the number of passengers trapped in elevators under earthquakes in urban areas is essential for promoting earthquake emergencies. A probability-based city-scale method for assessing the earthquake-induced risk of passenger entrapment in elevators was proposed, in which city-scale time history analysis was performed to simulate the seismic response of building clusters, and the Monte Carlo simulation was conducted to consider the uncertainty of multiple factors, including the mechanical properties of buildings and elevators, the elevator story position, and the spatiotemporal nature of elevator traffics. A case study of the Tsinghua University campus was performed to demonstrate the practicability of the method. The results show that the number of trapped passengers when an earthquake occurs during the off-peak hours of elevator traffic is approximately a quarter of that when the earthquake occurs at 8:00. The maximum number of trapped passengers under the maximum considered earthquake reaches 195, approximately five times that under the design basis earthquake. This study fills a gap in the research on city-scale earthquake-induced passenger entrapment risk. The proposed method can be used to perform both scenario- and intensity-based assessments, thereby having the potential to facilitate virtual rescuer drills and earthquake emergency plans.
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Harantová, Veronika, Jaroslav Mazanec, Vladimíra Štefancová, Jaroslav Mašek, and Hana Brůhová Foltýnová. "Two-Step Cluster Analysis of Passenger Mobility Segmentation during the COVID-19 Pandemic." Mathematics 11, no. 3 (January 22, 2023): 583. http://dx.doi.org/10.3390/math11030583.
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In this paper, we analyse the specific behaviour of passengers in personal transport commuting to work or school during the COVID-19 pandemic, based on a sample of respondents from two countries. We classified the commuters based on a two-step cluster analysis into groups showing the same characteristics. Data were obtained from an online survey, and the total sample size consists of 2000 respondents. We used five input variables, dividing the total sample into five clusters using a two-step cluster analysis. We observed significant differences between gender, status, and car ownership when using public transport, cars, and other alternative means of transportation for commuting to work and school. We also examined differences between individual groups with the same socioeconomic and socio-demographic factors. In total, the respondents were classified into five clusters, and the results indicate that there are differences between gender and status. We found that ownership of a prepaid card for public transport and social status are the most important factors, as they reach a significance level of 100%, unlike compared to other factors with importance ranging from 60 to 80%. Moreover, the results demonstrate that prepaid cards are preferred mainly by female students. Understanding these factors can help in planning transport policy by knowing the habits of users.
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Rezapour, Mahdi, and Khaled Ksaibati. "The Latent Class Multinomial Logit Model for Modeling Front-Seat Passenger Seatbelt Choice, Considering Seatbelt Status of Driver." Future Transportation 1, no. 3 (October 13, 2021): 559–69. http://dx.doi.org/10.3390/futuretransp1030029.
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The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings.
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Zhang, Xiaomin, Gohar Azhar, Emmanuel D. Williams, Steven C. Rogers, and Jeanne Y. Wei. "MicroRNA Clusters in the Adult Mouse Heart: Age-Associated Changes." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/732397.
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The microRNAs and microRNA clusters have been implicated in normal cardiac development and also disease, including cardiac hypertrophy, cardiomyopathy, heart failure, and arrhythmias. Since a microRNA cluster has from two to dozens of microRNAs, the expression of a microRNA cluster could have a substantial impact on its target genes. In the present study, the configuration and distribution of microRNA clusters in the mouse genome were examined at various inter-microRNA distances. Three important microRNA clusters that are significantly impacted during adult cardiac aging, the miR-17-92, miR-106a-363, and miR-106b-25, were also examined in terms of their genomic location, RNA transcript character, sequence homology, and their relationship with the corresponding microRNA families. Multiple microRNAs derived from the three clusters potentially target various protein components of the cdc42-SRF signaling pathway, which regulates cytoskeleton dynamics associated with cardiac structure and function. The data indicate that aging impacted the expression of both guide and passenger strands of the microRNA clusters; nutrient stress also affected the expression of the three microRNA clusters. The miR-17-92, miR-106a-363, and miR-106b-25 clusters are likely to impact the Cdc42-SRF signaling pathway and thereby affect cardiac morphology and function during pathological conditions and the aging process.
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Handoko, Koko. "PENGELOMPOKKAN DATA MINING PADA JUMLAH PENUMPANG DI BANDARA HANG NADIM." Computer Based Information System Journal 6, no. 2 (September 30, 2018): 60. http://dx.doi.org/10.33884/cbis.v6i2.708.
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The concept of data mining becomes one of the important tools in information management because the existing information has an increasing number. Data mining has many techniques in practice, one of which is the clustering technique which is the process of grouping data into groups so that data exist in the same group have properties as closely as possible. Clustering has many different methods, one of which is K-Means. By using ata mining clustering on traffic activity data taken from Hang Nadim Airport Batam, it can be obtained by grouping passenger based on clusters according to the nature of each data. The data taken include the number of passengers coming, departing, and transiting. In the process of performing data mining clustering, existing sample data must go through several important stages in order to get the correct cluster results. Stages that must be passed the Stages of Data Processing, Clustering Stage and Stage Algorithm. Based on the results of research that has been done on the existing sample data, it can be concluded the results of data grouping of passengers at Hang Nadim Airport Batam.
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Solntsev, Aleksey, Valeriy Zenchenko, Vitaly Guliy, Oyifien Ozaka Francis, Viacheslav Bezymyannyy, and Valentin Silyanov. "The Analytical Approaches and Principles Used for the Purchases of Light Weight and Passenger Vehicles in a Saturated Market." MATEC Web of Conferences 334 (2021): 01025. http://dx.doi.org/10.1051/matecconf/202133401025.
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Wibowo, Arief, Moh Makruf, Inge Virdyna, and Farah Chikita Venna. "Penentuan Klaster Koridor TransJakarta dengan Metode Majority Voting pada Algoritma Data Mining." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (June 26, 2021): 565–75. http://dx.doi.org/10.29207/resti.v5i3.3041.
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The Covid-19 pandemic has made many changes in the patterns of community activity. Large-Scale Social Restrictions were implemented to reduce the number of transmission of the virus. This clearly affects the mode of transportation. The mode of transportation makes new regulations to reduce the number of passenger capacities in each fleet, for example, TransJakarta services. This study will categorize the TransJakarta corridors before and during the Covid-19 pandemic. The clustering method of K-Means and K-Medoids is used to obtain accurate calculation results. The calculations are performed using Microsoft Excel, Rapid Miner, and Python programming language. The clustering results obtained that using K-Means algorithm before Covid-19 pandemic, an optimum number of clusters is 3 clusters with DBI (Davies Bouldin Index) value is 0.184, and during Covid-19 pandemic, the optimum number of clusters is 2 clusters with DBI value is 0.188. Meanwhile, when using the K-Medoids algorithm before the Covid-19 pandemic, an optimum number of clusters is 3 clusters with the DBI value is 0.200, and during the Covid-19 pandemic, an optimum number of clusters is 4 clusters with the DBI value is 0.190. The final cluster is determined using the majority voting approach from all the tools used.
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Kressner, Josephine D., and Laurie A. Garrow. "Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta, Georgia, Airport." Transportation Research Record: Journal of the Transportation Research Board 2266, no. 1 (January 2012): 20–30. http://dx.doi.org/10.3141/2266-03.
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This research investigated the influence of demographic and socio-economic factors on air travel demand by using a unique data set purchased from a credit-reporting agency. Linear regression models based on lifestyle segmentation variables were used to predict air passenger trips for Hartsfield–Jackson International Airport in Atlanta, Georgia. The study focused on predicting trips that originated from or terminated at residences in Atlanta's 13-county metropolitan area. The lifestyle regression models were compared with regression models based on income, because the latter were similar to the regression models currently used by the Atlanta Regional Commission to predict home-based airport passenger trips. The results provide directional evidence for using lifestyle clusters over income groups in predicting airport passenger trips. The evidence suggests that alternative data sources with adequate information for lifestyle segmentation can improve airport passenger models. The discussion points out the need for air passenger surveys to collect information about the number of annual air trips a surveyed individual takes.
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Ruiz Colmenares, Jon Ander, Estibaliz Asua Uriarte, and Inés del Campo. "Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques." Applied Sciences 13, no. 3 (January 23, 2023): 1510. http://dx.doi.org/10.3390/app13031510.
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Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed.
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Oguntona, Oluwaferanmi, Kay Ploetner, Marcia Urban, Raoul Rothfeld, and Mirko Hornung. "IMPACT OF AIRLINE BUSINESS MODELS, MARKET SEGMENTS AND GEOGRAPHICAL REGIONS ON AIRCRAFT CABIN CONFIGURATIONS." Journal of Air Transport Studies 10, no. 1 (January 1, 2019): 1–38. http://dx.doi.org/10.38008/jats.v10i1.8.
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Besides the significance of estimating aircraft seat capacity for airline operating cost and yield estimation as well as for the conceptual design of aircraft, airline fleet planning requires an understanding of aircraft cabin configuration. This paper presents the impact of airline business models, market segments in terms of flight distances, and geographical regions on aircraft cabin configuration, i.e. aircraft seat capacities and installed seats per cabin class. Using the historical databases of global low-cost carriers and airline flight schedules between 2000 and 2016, two ABM clusters – full-service network carriers (FSNCs) and low-cost carriers (LCCs) - were developed, while using seven already-developed passenger-aircraft clusters. Focusing on the jet commuter (JC), narrow-body (NB) and long-range (LR) aircraft clusters, studies were conducted on the historical development of aircraft cluster seat capacities at different abstraction levels: global, airline business model, intra- and inter-regional flight distances, as well as a combination of ABM and (inter)regional flights. Selected results were further analysed using statistical tests on the mean and regression analysis. The analysis results show that LCCs use aircraft that have less average scheduled and less average maximum possible seats than FSNCs. Specifically, FSNCs use significantly bigger aircraft types in LR cluster than LCCs, while LCCs use significantly bigger aircraft types in JC cluster than FSNCs. Furthermore, average cabin utilisation of aircraft clusters scheduled by LCCs are significantly higher than average cabin utilisation scheduled by FSNCs. With increasing distance, average cabin utilisation also significantly reduces.
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Wijayanto, Y., A. Fauzi, E. Rustiadi, and Syartinilia. "Spatial Patterns Analysis of Jabodetabek Electric Rail Transportation Using Spatial Autocorrelation Approach." IOP Conference Series: Earth and Environmental Science 950, no. 1 (January 1, 2022): 012082. http://dx.doi.org/10.1088/1755-1315/950/1/012082.
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Abstract This study investigates the density level of the Jabodetabek KRL stations and routes and the spatial pattern of the Jabodetabek KRL. The method used is spatial autocorrelation calculation using the Moran’s Index putting on data PT KCI from 2014 through 2020, spatial data from BIG, and BPS population data. The study results show that the stations and the routes were congested. Still, there was a drastic decrease in passengers when the Covid-19 outbreak entered Indonesia in March 2020. There was positive autocorrelation and spatial patterns forming Clusters which means that it is necessary to create new lines and stations to break up the current overcrowding of train passengers. As a result, understanding the density level of stations and routes and the passenger density distribution pattern at Jabodetabek stations may be utilized to design the building of new stations and lines at KRL Jabodetabek, thereby increasing the degree of comfort, safety, and security.
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Frutos-Bernal, Elisa, Ángel Martín del Rey, Irene Mariñas-Collado, and María Teresa Santos-Martín. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition." Mathematics 10, no. 7 (March 31, 2022): 1122. http://dx.doi.org/10.3390/math10071122.
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In recent years, a growing number of large, densely populated cities have emerged, which need urban traffic planning and therefore knowledge of mobility patterns. Knowledge of space-time distribution of passengers in cities is necessary for effective urban traffic planning and restructuring, especially in large cities. In this paper, the inbound ridership in the Barcelona metro is modelled into a three-way tensor so that each element contains the number of passenger in the ith station at the jth time on the kth day. Tucker3 decomposition is used to discover spatial clusters, temporal patterns, and the relationships between them. The results indicate that travel patterns differ between weekdays and weekends; in addition, rush and off-peak hours of each day have been identified, and a classification of stations has been obtained.
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Sekizuka, Tsuyoshi, Kentaro Itokawa, Tsutomu Kageyama, Shinji Saito, Ikuyo Takayama, Hideki Asanuma, Naganori Nao, et al. "Haplotype networks of SARS-CoV-2 infections in theDiamond Princesscruise ship outbreak." Proceedings of the National Academy of Sciences 117, no. 33 (July 28, 2020): 20198–201. http://dx.doi.org/10.1073/pnas.2006824117.
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TheDiamond Princesscruise ship was put under quarantine offshore Yokohama, Japan, after a passenger who disembarked in Hong Kong was confirmed as a coronavirus disease 2019 case. We performed whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) directly from PCR+clinical specimens and conducted a phylogenetic analysis of the outbreak. All tested isolates exhibited a transversion at G11083T, suggesting that SARS-CoV-2 dissemination on theDiamond Princessoriginated from a single introduction event before the quarantine started. Although further spreading might have been prevented by quarantine, some progeny clusters could be linked to transmission through mass-gathering events in the recreational areas and direct transmission among passengers who shared cabins during the quarantine. This study demonstrates the usefulness of haplotype network/phylogeny analysis in identifying potential infection routes.
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Faroqi, Hamed, Mahmoud Mesbah, and Jiwon Kim. "Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data." Mathematical Problems in Engineering 2019 (April 14, 2019): 1–16. http://dx.doi.org/10.1155/2019/5070794.
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Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).
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Pavlyuk, Dmitry, Nadežda Spiridovska, and Irina Yatskiv (Jackiva). "SPATIOTEMPORAL DYNAMICS OF PUBLIC TRANSPORT DEMAND: A CASE STUDY OF RIGA." Transport 35, no. 6 (January 6, 2021): 576–87. http://dx.doi.org/10.3846/transport.2020.14159.
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Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.
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Seo, Younghoon, Donghyun Lim, Woongbee Son, Yeongmin Kwon, Junghwa Kim, and Hyungjoo Kim. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea." Sustainability 12, no. 24 (December 15, 2020): 10482. http://dx.doi.org/10.3390/su122410482.
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Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.
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Lin, Mu, Zhengdong Huang, Tianhong Zhao, Ying Zhang, and Heyi Wei. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data." Sustainability 14, no. 15 (August 3, 2022): 9564. http://dx.doi.org/10.3390/su14159564.
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Automated fare collection (AFC) systems can provide tap-in and tap-out records of passengers, allowing us to conduct a comprehensive analysis of spatiotemporal patterns for urban mobility. These temporal and spatial patterns, especially those observed over long periods, provide a better understanding of urban transportation planning and community historical development. In this paper, we explored spatiotemporal evolution of travel patterns using the smart card data of subway traveling from 2011 to 2017 in Shenzhen. To this end, a Gaussian mixture model with expectation–maximization (EM) algorithm clusters the travel patterns according to the frequency characteristics of passengers’ trips. In particular, we proposed the Pareto principle to negotiate diversified evaluation criteria on model parameters. Seven typical travel patterns are obtained using the proposed algorithm. Our findings highlighted that the proportion of each pattern remains relatively stable from 2011 to 2017, but the regular commuting passengers play an increasingly important position in the passenger flow. Additionally, focusing on the busiest commuting passengers, we depicted the spatial variations over years and identified the characters in different periods. Their cross-year usage of smart cards was finally examined to understand the migration of travel patterns over years. With reference to these methods and insights, transportation planners and policymakers can intuitively understand the historical variations of passengers’ travel patterns, which lays the foundation for improving the service of the subway system.
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Gemra, Stanisław. "The essence and importance of clusters in the management of transport companies." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 18, no. 11 (November 30, 2017): 30–33. http://dx.doi.org/10.24136/atest.2017.043.
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The aim of the paper is to show the structure of the cluster, the assumptions embedded in the concept car transport companies and to present their own research in the field of express willingness to join the cluster and create a new cluster of passenger transport by Automotive Communications Enterprise. Based on the results presented in the article surveys, it should be noted that the car transport companies show a high level of interest in cooperation with other actors in the sector in the form of the cluster.
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Enin, D. V. "Approaches to Determining the Regular Transit Route Duplication Level." World of Transport and Transportation 19, no. 1 (September 8, 2021): 210–28. http://dx.doi.org/10.30932/1992-3252-2021-19-1-210-228.
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Issues of duplication of regular transit routes are of particular importance in the field of transport services provided to population and organisation of passenger transportation from the perspective of ensuring compliance with passengers needs for transportation and of the effects of route duplication on the technical, operational, and economic indicators of performance of these routes and the integral route network.In Russia duplication of regular routes within route networks emerged in the late 1990s – early 2000s in urban transit, other transit modes, and in interconnected transit. In the last decade, these routes have been increasingly subject to revision by local governments and executive bodies of federal constituent entities of the Russian Federation while solving transport planning problems and improving quality of transport services for the population.Evaluation of route duplication, as a rule, is carried out based on the route factor and the route duplication factor, the latter allows pairwise assessment of routes by the length of their overlapping segments.The objective of this article is to show incorrectness of the widespread technique and to present another approach that provides, in the author’s opinion, the correct interpretation of the method for determining the route duplication rate. Achieving this objective is based on methods of theoretical research in the field of organising passenger transportation.In development of this logic, the author proposed a new method for determining the route duplication factor using route adjacency factor, which considers directions and volumes of passenger origin-destination flows. Comparison of existing and proposed approaches is given using simple examples. The results of calculations have confirmed the different nature of factors and the absence of a direct relationship between the needs of passengers for transportation by public transport and the length of adjacent sections of routes. The conclusion is made about probable expediency of using the second (author’s) approach based on the route adjacency factor, which provides a correct solution to the stated transport planning problem. Besides, the possibility of using a new approach when performing diagnostics or designing route networks of different transport modes is shown both in relation to route matching and regarding their clusters and the entire route network.
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Lin, Pengfei, Jiancheng Weng, Dimitrios Alivanistos, Siyong Ma, and Baocai Yin. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data." Sustainability 12, no. 12 (June 19, 2020): 5010. http://dx.doi.org/10.3390/su12125010.
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Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly concentrated in the low housing price and high or medium population density areas; subway facilities will promote people to commute using public transport. This study will help stakeholders optimize the public transport networks, scheduling scheme, and policy accordingly, thus ameliorating commuting within cities.
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Rahmanov, Farhad, Lala Neymatova Lala Neymatova, Ramilya Aliyeva, and Albina Hashimova. "Management of the Transport Infrastructure of Global Logistics: Cross-Country Analysis." Marketing and Management of Innovations 13, no. 4 (2022): 65–75. http://dx.doi.org/10.21272/mmi.2022.4-07.
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In modern society, there is a constant development and improvement of the transport industry, due to which the role and distribution of this logistics industry, which is a service, is growing for the high-quality and fast delivery of goods. To maximize the export of finished products and more effective penetration into international markets around the world are organized by global logistics systems. This article summarizes the arguments and counterarguments within the scientific discussion on the place and prospects of management of the transport infrastructure of global logistics. The study’s main purpose is to confirm the hypothesis about the existence of global logistics clusters united by a common transport infrastructure in accordance with the geopolitical and economic features of the regions. In this regard, the array of input data is presented in the form of ten transport infrastructure indicators from databases of the World Bank and Organisation for Economic Co-operation and Development. The study of the transport infrastructure of global logistics in the article is carried out in the following logical sequence: the formation of an array of input data; input data normalization; determination of the integral index of the level of transport infrastructure’s development (principal component method); clustering (the k-means method) and interpretation of the obtained results. Forty-five European and Asian countries were selected as the object of the study from 2006 to 2020. The study empirically confirms the above hypothesis, evidenced by the identified integral index of the level of transport infrastructure’s development and qualitative composition of the obtained clusters. The road passenger transport indicator exerts the most significant influence on the integral index of transport infrastructure, air transport, passengers carried, container port traffic and railways, passengers carried. In general, during the studied period, countries were grouped into three and two clusters. The consolidation of clusters in 2020 indicates that the transport infrastructure of countries with an average level of economic development began to develop actively. In particular, this concerns the increased demand for road transport. The study results can be useful for public authorities and international organizations that provide services for managing the transport infrastructure of global logistics.
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Maulit, A., A. S. Tlebaldinova, A. B. Nugumanova, and Ye M. Baiburin. "Computer Modelling of Temporal Networks for Bike Sharing Usage Patterns Analysis." Izvestiya of Altai State University, no. 4(114) (September 9, 2020): 102–7. http://dx.doi.org/10.14258/izvasu(2020)4-16.
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This paper presents the results of analyzing the time load of stations in bike-sharing systems using temporal networks. Temporal networks have many applications in the study of the behavior of complex dynamic systems that have a network structure. In particular, they can be used to analyze and predict many dynamic indicators of transport networks, for example, such as the intensity of transport and passenger flows, traffic congestion, capacity of transport nodes, turnover of vehicles, etc. In this work, the indicators of the centrality of stations and clusters of a bike-sharing network are estimated using temporal networks. Based on the obtained estimates, visual models (Heat maps and Time Series) are constructed to demonstrate the spatial and temporal features of the bike network in a clear and compact form. The station centralities are estimated on the basis of the betweenness measure, and the cluster centralities are estimated on the basis of the Freeman centralization. Experiments confirming the applicability of the built models are conducted using open data from the CitiBike New York system for April 2019. They demonstrated the presence of daily and monthly patterns among both individual stations and more large station clusters.
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Natalia, Bonifasia Ekta Fima. "Airline Collocation: Frequency Based Analysis with COCA as a Corpus." JET ADI BUANA 8, no. 01 (April 30, 2023): 55–68. http://dx.doi.org/10.36456/jet.v8.n01.2023.7099.
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Collocations are words that co-occur together in any text and have definite association. The use of collocations is part of linguistic awareness which defines how language is used naturally. This study aims to analyze the collocations used in airlines by using Corpus of Contemporary American English (COCA). The data were analyzed both quantitatively and qualitatively. Quantitatively, the data were calculated regarding the frequency, topics and cluster. Qualitatively, the data were verbally analyzed, described, and discussed. The big data is taken from 1990-2019. The frequent words in area of airlines management found in COCA are used as keywords to find concordance of other words or phrases. The results of the study shows that from the three words taken from the script of flight announcement in airport ‘flight’, ‘passenger’, and ‘boarding’ it was found that the frequency risen is 73,870. The topics related to the three words of airlines found is 22,798. The clusters found from the three words by COCA is 34,006 which ‘flight’ word has the largest clusters (18,730). From the COCA analysis, the new words or idioms related to airlines can be used as teaching material in vocabulary for aviation or airlines students.
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Dewulf, Wouter, Hilde Meersman, and Eddy Van de Voorde. "FROM CARPET SELLERS TO CARGO STARS: ANALYZING STRATEGIES OF AIR CARGO CARRIERS." Journal of Air Transport Studies 5, no. 1 (January 1, 2014): 96–119. http://dx.doi.org/10.38008/jats.v5i1.75.
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While some research has been done on passenger airlines strategy, the strategies of air cargo carriers have hardly been researched. This paper analyses and compares the strategies of air cargo carriers. Therefore, a typology of management strategies for both combination and full cargo airlines has been developed, in which the various strategy choices within the strategic framework of the respective air cargo carriers are further elaborated. The typology has been developed through a K-means cluster analysis on a data set of 47 air cargo carriers. The use of a cluster analysis to group the strategy models of a number of air cargo carriers is a novel feature of this research. The results of this research generate a typology of seven representative clusters of air cargo carriers’ strategy models, each with their own characterizing features. Striking differences and similarities are highlighted. Our findings suggest the clear existence of different strategy models and the differing degree of focus on air cargo strategy development and deployment among the air cargo carriers’ population.
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Bychkova, A. A. "Optimisation of Russian railways’ high-speed routes in the regions." Vestnik Universiteta 1, no. 7 (August 31, 2022): 82–89. http://dx.doi.org/10.26425/1816-4277-2022-7-82-89.
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Rail transport is constantly in development and improvement, as it is one of the most important strategic means of transportation. That is why the optimization of Russian railway routes in this case is an integral element of development. The introduction of a high-speed passenger transportation route makes it possible to increase throughput capacity, improve service conditions, and strengthen the position in the industry. The article points out the necessity of optimising the route from the Ural to the Volga federal districts. The relevance of the topic under study lies in the advantage of railway transport in interspecific competition in the market for the provision of passenger transportation services. The author used the local Moran index in the analysis, studied regional interconnections, and identified clusters. The results of the study are visually represented in the figure, where the dotted line highlights the potential route of high-speed traffic Yekaterinburg–Kazan. The article describes the possible risks of implementing this route, cites optimization expediency factors (increase of interspecific competition).
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Tang, Hongjiu. "Regional Patterns and Hierarchical Tendencies: Analysis of the Network Connectivity of 63 Service-Oriented Tourist Cities in China." Sustainability 12, no. 16 (August 12, 2020): 6532. http://dx.doi.org/10.3390/su12166532.
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Previous studies of service-oriented tourist city networks have often focused on the analysis of the geographical distributions and service roles of important cities instead of the connections and hierarchical tendencies between different types of cities within a whole region. The current study uses big data approaches for the regional connections of 38 tourism organizations, including famous hotels, air passenger transport services, and tourism service agencies, across 63 of the most important tourist cities in China. Fuzzy c-means clustering analysis is used to define eight city arena clusters. According to the distributions of connectivity between the 63 cities, these eight clusters play different functional service roles in the urban tourism network in four hierarchies. With their “center–edge” memberships, these arena clusters are formed by the interweaved process of regional and hierarchical tourism service connections. The results here include analysis of the various service-oriented tourist cities in China and point out the geographical “gap” faced by networks. Service-oriented tourist cities need to find their hierarchies and positioning in the network, scientifically speaking, to avoid blind development and to support sustainable regional tourism development in urban areas.
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GEGOV, EMIL, M. NADIA POSTORINO, MARK ATHERTON, and FERNAND GOBET. "COMMUNITY STRUCTURE DETECTION IN THE EVOLUTION OF THE UNITED STATES AIRPORT NETWORK." Advances in Complex Systems 16, no. 01 (March 2013): 1350003. http://dx.doi.org/10.1142/s0219525913500033.
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This paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin–destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration.
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Goldsberry, Leah, and Adam R. Scavette. "Exploiting a Natural Hub: Turning a Stopover into a Destination." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 23 (April 3, 2018): 8–14. http://dx.doi.org/10.1177/0361198118758983.
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In the mature aviation system of today, it is difficult to establish new hubs that focus solely on transfer traffic. This paper identifies a new type of hub—a natural tourism hub—one at which an airline and the surrounding metropolitan area can simultaneously benefit from a transportation hub and accompanying tourist destination, respectively. The study aims to identify existing airports for these stopover locations that are located on highly trafficked international flight routes. Using Iceland as an example, this country’s success in optimizing its stopover location to promote tourism and gain airline passenger demand is examined. The analysis is carried out by implementing a k-means clustering algorithm on total distance added for stopover locations, as well as flight leg symmetry to identify existing airports that are geographically located in an optimal stopover path for international routes across the Atlantic and Pacific oceans, and between Europe and East Asia. Airports in the clusters that minimize total added distance are then observed, and the clusters are ordered based on how symmetric the two flight legs of a stopover journey at an airport in that cluster tend to be. In addition, three airports near the top of this list are analyzed as potential stopover locations. In using this algorithm, not only is it possible to forecast which hubs may become major tourist destinations, but also to identify how airlines can shape people’s perception of their location as a tourist destination.
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Vallet, Flore, Mostepha Khouadjia, Ahmed Amrani, and Juliette Pouzet. "DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK." Proceedings of the Design Society 1 (July 27, 2021): 1093–102. http://dx.doi.org/10.1017/pds.2021.109.
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AbstractMassive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.
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Il Kim, Kwang, and Keon Myung Lee. "Mining of missing ship trajectory pattern in automatic identification system." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 167. http://dx.doi.org/10.14419/ijet.v7i2.12.11117.
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Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.
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Markov, Leonid S., and Vasiliy S. Plotnikov. "Cluster approach and tourism development in the Novosibirsk region." World of Economics and Management 20, no. 4 (2020): 5–24. http://dx.doi.org/10.25205/2542-0429-2020-20-4-5-24.
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The study focuses on the main trends in the tourism industry of the Novosibirsk region. It is shown that the regional industry is developing in line with the national trends and falls with the general development trends of domestic and inbound tourism set forth in the documents of strategic development at the federal and sub-federal levels. The article discusses the aspects of the tourism industry, which determine the specifics of its functioning related to the geographical concentration, heterogeneity, and multiplicity of participants as well as the complex nature of the tourist product. It is concluded that the application of cluster approach to tourism development seems rather appropriate. The work determines priority types of tourism and clusters to be formed and developed in the Novosibirsk region. Besides, the article describes the factors, opportunities and restrictions for tourism development in Novosibirsk region in terms of demand. It has been shown that the expectations regarding the development of domestic tourism against the background of global failure of international passenger traffic are only partially justified since hardly can Russian domestic tourism be an equivalent substitution for the outbound tourism. Under these circumstances, the cluster tourism policy should mainly focus on the following priority directions: domestic and mainly short-term tourism; increase in the activity of tour operators in the formation of regional tourist products; development of tourism and infrastructure facilities.
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Тлегенов and B. Tlegenov. "The analysis of the indices verify cluster solutions." Modeling of systems and processes 6, no. 4 (January 21, 2014): 65–69. http://dx.doi.org/10.12737/4051.
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Jivaramonaikul, Thananan, Pilailuk Akkapaiboon Okada, Nuengruethai Srisong, Watcharee Kanchana-udom, and Pantila Taweewigyakarn. "Investigation of a COVID-19 Cluster Suspected In-flight Transmission, December 2020." Outbreak, Surveillance, Investigation & Response (OSIR) Journal 16, no. 1 (March 31, 2023): 7–13. http://dx.doi.org/10.59096/osir.v16i1.262096.
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On 1 Dec 2020, the Thai Department of Disease Control was notified of five COVID-19 infections among passengers on a flight from Switzerland to Thailand. The objectives of this investigation were to confirm the outbreak, describe epidemiological characteristics, and identify the source of infection. We performed a descriptive study and contact tracing among the flight’s passengers. We interviewed the cases and reviewed their medical records, as well as an environmental survey of the state quarantine facility. Whole genome sequencing to determine the percentage alignment identity for RT-PCR-positive cases was conducted. Thirteen infected passengers out of 107 people on the flight (12.1%) were identified. The suspected index case was a symptomatic passenger, non-mask-wearing passenger. Five of the 13 confirmed cases shared a similar genomic pattern (98–100% alignment identity), and four cases sat within one row either in front of or behind the suspected index case. The genomes of the cases were more similar to each other than those uploaded to the GISAID database from Switzerland. The symptomatic COVID-19 passenger without mask wearing was suspected to be the source. Mask wearing should be mandated on flight to prevent spreading of respiratory infectious diseases.
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Carcellar III, B. G., A. C. Blanco, and M. Nagai. "SPATIAL AND TEMPORAL COMMUNITY DETECTION OF CAR MOBILITY NETWORK IN METRO MANILA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 101–8. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-101-2019.
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Abstract. Transportation Network Companies (TNCs) like Uber utilize GPS and wireless connection for passenger pickup, driver navigation, and passenger drop off. Location-based information from Uber in aggregated form has been made publicly available. They capture instantaneous traffic situation of an area, which makes describing spatiotemporal traffic characteristics of the area possible. Such information is valuable, especially in highly urbanized areas like Manila that experience heavy traffic. In this research, a methodology for identifying the underlying city structure and traffic patterns in Metro Manila was developed from the Uber trip information. The trip information was modelled as a complex network and Infomap community detection was utilized to group areas with ease of access. From Uber trip dataset, the data was segregated into different hours-of-day and for each hour-of-day, a directed-weighted temporal network was generated. Hours-of-day with similar traffic characteristics were also grouped together to form hour groups. From the results of the network characterization, hours-of-day were grouped into six hour groups; 00 to 04 hours-of-day in hour group 1, 05 to 07 hours-of-day in group 2, 08 to 12 hours-of-day in group 3, 13 to 15 in group 4, 16 to 19 in group 5, and 20 to 23 in group 6. Major roads as well as river networks were observed to be the major skeleton and boundaries of the generated clusters.
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Stapleton, Drew, Melissa Cooley, Darlene Goehner, and Daloud Jandal. "The case for U.S. high speed rail." Journal of Transportation Management 13, no. 1 (April 1, 2002): 29–40. http://dx.doi.org/10.22237/jotm/1017619500.
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High-speed rail is a form of self-guided ground transportation, which utilizes steel-wheels or magnetic levitation (i.e., Maglev) and can travel in excess of 200 miles per hour. High-speed ground transportation (i.e., HSGT) has been widely used in Europe and Asia, but the debate continues over the usefulness of high-speed rail in the United States. Several metropolitan areas in the United States have been identified as corridors that would benefit from HSGT. High speed rail can offer an alternative or a compliment to over-the-road and air transportation. Initial investment cost for this mode of transportation are high, but other factors such as fewer emissions from trains help to balance these costs. This manuscript examines the feasibility of bringing high-speed rail to clusters of cities throughout the United States (i.e., corridors) for passenger and commercial freight transportation.
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O'Toole, Áine, Verity Hill, Oliver G. Pybus, Alexander Watts, Issac I. Bogoch, Kamran Khan, Jane P. Messina, et al. "Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2." Wellcome Open Research 6 (May 19, 2021): 121. http://dx.doi.org/10.12688/wellcomeopenres.16661.1.
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Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.
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Gkoumas, Konstantinos. "Hyperloop Academic Research: A Systematic Review and a Taxonomy of Issues." Applied Sciences 11, no. 13 (June 26, 2021): 5951. http://dx.doi.org/10.3390/app11135951.
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Hyperloop is a proposed very high-speed ground transportation system for both passenger and freight that has the potential to be revolutionary, and which has attracted much attention in the last few years. The concept was introduced in its modern form relatively recently, yet substantial progress has been made in the past years, with research and development taking place globally, from several Hyperloop companies and academics. This study examined the status of Hyperloop development and identified issues and challenges by means of a systematic review that analyzed 161 documents from the Scopus database on Hyperloop since 2014. Following that, a taxonomy of topics from scientific research was built under different physical and operational clusters. The findings could be of help to transportation academics and professionals who are interested in the developments in the field, and form the basis for policy decisions for the future implementation of Hyperloop.
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O'Toole, Áine, Verity Hill, Oliver G. Pybus, Alexander Watts, Issac I. Bogoch, Kamran Khan, Jane P. Messina, et al. "Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 with grinch." Wellcome Open Research 6 (September 17, 2021): 121. http://dx.doi.org/10.12688/wellcomeopenres.16661.2.
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Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.
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Di Loreto, Samantha, Fabio Serpilli, and Valter Lori. "Soundscape Approach in the Seaport of Ancona: A Case Study." Acoustics 4, no. 2 (June 14, 2022): 492–516. http://dx.doi.org/10.3390/acoustics4020031.
Full textAbstract:
Today, the art of knowing how to listen is more urgent than ever. The perceptive sound system of the human being is stimulated daily by countless artificial sounds that dominate natural ones. When it comes to the idea of the soundscape, the terminology was initially referred to by composer and environmentalist Raymond Murray Shafer, who defined “soundscape” as a relationship between the ear, humans, built environments, and society. This paper aims to apply the sound landscape approach in the seaport area of Ancona (Italy); a large area, frequented daily by many people, which is divided into passenger and ferry terminals, container terminals, plants for solid bulk, and commercial and recreational activities. The purpose of the study was to evaluate the perception that a human has of the urban layout of the port area by correlating the parameters of traditional acoustics with psychoacoustics. To evaluate the subjective parameters, a questionnaire was developed and applied, enabling the analysis of demographic and behavioral factors such as age, visit frequency, and length of stay of the participants in the clusters of the seaport. This way, it was possible to give an indication of the sound quality of the different clusters of the port area, from an acoustic and emotional point of view, and this information could be particularly helpful in the planning phase of new attractions for the Ancona seaport.
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Chen, Chen, and Yuanchang Xie. "Machine Learning for Recognizing Driving Patterns of Drivers of Large Commercial Trucks." Transportation Research Record: Journal of the Transportation Research Board 2517, no. 1 (January 2015): 18–27. http://dx.doi.org/10.3141/2517-03.
Full textAbstract:
Commercial large truck crashes are more likely to involve fatalities and significant costs than passenger vehicle crashes are. To reduce fatigue-related crashes of large trucks caused by drivers' irregular work schedules, FMCSA has enforced hours-of-service rules to regulate the activities of drivers of commercial large trucks. The complex influence of drivers' multiday driving activity patterns on crash risk was examined with data collected from two national truckload carriers. A machine learning approach, k-means clustering, was used to classify large truck drivers into 10 clusters according to their 15-min driving activities over multiple days. Then, the crash risk and driving activity pattern were identified for each cluster. Discrete-time logistic regression models were used to quantify the relationships between driving activity patterns and crash risk. Results indicated that the driving pattern with the lowest crash risk could be daytime driving between 4:00 a.m. and noon, with rest breaks in the late afternoon (4:00 to 6:00 p.m.). Drivers with high proportions of afternoon on-duty time after a long off-duty period experienced significantly higher crash risk. A representative day concept is proposed as a complementary method to identify relationships between driving patterns and crash risk. Moreover, on-duty hours can be a useful indicator of crash risk for drivers of large trucks. High proportions of on-duty hours in the early morning and late afternoon often are associated with high crash risk.