Academic literature on the topic 'Passenger clusters'
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Journal articles on the topic "Passenger clusters"
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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.
Dissertations / Theses on the topic "Passenger clusters"
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Ji, Qunfeng. "Investigating low carbon development of high-density building clusters located around railway passenger transport hubs in China." Thesis, Cardiff University, 2018. http://orca.cf.ac.uk/117955/.
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China has experienced high rates of urbanisation due to the increasing housing demand in cities, resulting in high energy consumption and high carbon dioxide emissions from buildings. Moreover, transport-related carbon dioxide emissions will also show a dramatic increase because of the growing number of vehicles in the process of the rapid urbanisation. This research aims to investigate building energy consumption and transport-related carbon dioxide emissions due to mobilities of users from buildings and propose strategies to reduce their energy demand and carbon dioxide emissions in cities. The main contributions of this research are two-fold. Firstly, in the theoretical aspect,this research fills the research gap on the combination of the carbon dioxide emissions quantification with buildings and the transport. Secondly, in the practical perspective, this research presents examples study of the carbon dioxide emissions quantification, analyses potential factors affecting energy consumption and carbon dioxide emissions, and provides strategies for low carbon city development. This study adopts an on-site survey, questionnaires, modelling simulation, and regression analysis to explore the situations of carbon dioxide emissions in three cases, with each representing one typical location type. The study provides an understanding of the low carbon city development, investigates energy demand and carbon dioxide emissions and compares energy demand with the simulation; it examines factors including street orientation, the layout of building clusters, overshadows, and urban heat island effects with carbon dioxide emissions from building sectors. Meanwhile, this study regresses modal splits with three aspects relating to socioeconomic characteristics, travel patterns from respondents, and self-evaluation on travelling. All of these provides implications for both theoretical and practical research on low carbon city development.
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Liu, Yulin. "Urban transit quality of service : user perception and behaviour." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61517/1/Yulin_Liu_Thesis.pdf.
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Despite its potential multiple contributions to sustainable policy objectives, urban transit is generally not widely used by the public in terms of its market share compared to that of automobiles, particularly in affluent societies with low-density urban forms like Australia. Transit service providers need to attract more people to transit by improving transit quality of service. The key to cost-effective transit service improvements lies in accurate evaluation of policy proposals by taking into account their impacts on transit users. If transit providers knew what is more or less important to their customers, they could focus their efforts on optimising customer-oriented service. Policy interventions could also be specified to influence transit users’ travel decisions, with targets of customer satisfaction and broader community welfare. This significance motivates the research into the relationship between urban transit quality of service and its user perception as well as behaviour.
This research focused on two dimensions of transit user’s travel behaviour: route choice and access arrival time choice. The study area chosen was a busy urban transit corridor linking Brisbane central business district (CBD) and the St. Lucia campus of The University of Queensland (UQ). This multi-system corridor provided a ‘natural experiment’ for transit users between the CBD and UQ, as they can choose between busway 109 (with grade-separate exclusive right-of-way), ordinary on-street bus 412, and linear fast ferry CityCat on the Brisbane River. The population of interest was set as the attendees to UQ, who travelled from the CBD or from a suburb via the CBD. Two waves of internet-based self-completion questionnaire surveys were conducted to collect data on sampled passengers’ perception of transit service quality and behaviour of using public transit in the study area.
The first wave survey is to collect behaviour and attitude data on respondents’ daily transit usage and their direct rating of importance on factors of route-level transit quality of service. A series of statistical analyses is conducted to examine the relationships between transit users’ travel and personal characteristics and their transit usage characteristics. A factor-cluster segmentation procedure is applied to respodents’ importance ratings on service quality variables regarding transit route preference to explore users’ various perspectives to transit quality of service. Based on the perceptions of service quality collected from the second wave survey, a series of quality criteria of the transit routes under study was quantitatively measured, particularly, the travel time reliability in terms of schedule adherence. It was proved that mixed traffic conditions and peak-period effects can affect transit service reliability.
Multinomial logit models of transit user’s route choice were estimated using route-level service quality perceptions collected in the second wave survey. Relative importance of service quality factors were derived from choice model’s significant parameter estimates, such as access and egress times, seat availability, and busway system. Interpretations of the parameter estimates were conducted, particularly the equivalent in-vehicle time of access and egress times, and busway in-vehicle time. Market segmentation by trip origin was applied to investigate the difference in magnitude between the parameter estimates of access and egress times. The significant costs of transfer in transit trips were highlighted. These importance ratios were applied back to quality perceptions collected as RP data to compare the satisfaction levels between the service attributes and to generate an action relevance matrix to prioritise attributes for quality improvement.
An empirical study on the relationship between average passenger waiting time and transit service characteristics was performed using the service quality perceived. Passenger arrivals for services with long headways (over 15 minutes) were found to be obviously coordinated with scheduled departure times of transit vehicles in order to reduce waiting time. This drove further investigations and modelling innovations in passenger’ access arrival time choice and its relationships with transit service characteristics and average passenger waiting time. Specifically, original contributions were made in formulation of expected waiting time, analysis of the risk-aversion attitude to missing desired service run in the passengers’ access time arrivals’ choice, and extensions of the utility function specification for modelling passenger access arrival distribution, by using complicated expected utility forms and non-linear probability weighting to explicitly accommodate the risk of missing an intended service and passenger’s risk-aversion attitude.
Discussions on this research’s contributions to knowledge, its limitations, and recommendations for future research are provided at the concluding section of this thesis.
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Naveen, B. R. "Service quality of Intercity Bus transport." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4972.
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Public transportation has become one of the cornerstones for country’s infrastructure development. Road transportation is one of the important means of public transportation. Within road transportation, a large number of people use bus transport as the means to commute between one place for work, home, visiting friends, trips etc. Ensuring service quality in this service, therefore, becomes imperative for the transportation services providers. The research objectives are as follows: (a) To study passenger patterns and passenger preferences with respect to service quality dimensions. (b) To explore the impact of demography, transportation, technology, policy and road infrastructure attributes on service quality factors and their impact on overall satisfaction of intercity bus transportation. (c) To attempt a cause-effect relationship model of service quality in relation with overall satisfaction with intercity bus transportation. (d) To understand the nature of intercity bus passenger transportation in European cities and compare with the Indian scenario.
The sample consists of 605 intercity bus transport passengers travelling within Karnataka, with starting journey from Bangalore. The data analysis is based on statistical techniques namely, reliability analysis, factor analysis, K-means cluster analysis, t-test, ANOVA, Tukey HSD post-hoc test, Games-Howell post-hoc test, regression techniques, partial least square structural equation modelling (PLS-SEM), multigroup analysis, mediation and moderation analysis.
Research findings showed that demography and transport attributes have significant differences in the perception of service quality dimensions and overall satisfaction. Passengers are clustered into three types based on their preferences. The first passenger type usually travels more than 350 kilometres by opting for the night journey and demand high service quality. The second type usually travels less than 50 kilometres, opts for day journey and has low demand for service quality. The third type of passenger usually travels more than 350 kilometres by opting day journey and they perceive service quality as moderate. While demography, policy and technology attributes significantly impact the service quality dimensions and overall satisfaction of intercity bus service, external tangibles like clean toilets and clean bus stands and environmental dimensions do not significantly impact the overall satisfaction.
Comparative study of the Indian and European context demonstrates technology interface as essential for passengers of both European and Indian context. However, environmental dimension significantly impacts the overall satisfaction of passengers in European context whereas it does not significantly impact the overall satisfaction of passenger in Indian context. Similarly, external tangibles such as clean drinking water, clean bus stops are important to passengers in European context, but not for passengers in the Indian context. This study contributes by developing passenger clusters based on their service quality preferences and identifying the influence of transport attributes on perception of service quality of transport
Book chapters on the topic "Passenger clusters"
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Xiao, Binjie. "A Passenger Flow Emergency System for Cluster Personnel Regional Based on Location Information." In Human Centered Computing, 672–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_67.
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Samuylov, Valery, Mikhail Petrov, and Tatyana Kargapoltseva. "A Model of Cluster-Modular Development of Passenger Traffic in the Urals Federal District, Russia." In VIII International Scientific Siberian Transport Forum, 1176–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37919-3_115.
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Akin, Darcin. "Evaluating Alternatives of Transport Network Design of a Metropolitan City Using Hierarchical Cluster Analysis." In Engineering Tools and Solutions for Sustainable Transportation Planning, 224–53. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2116-7.ch010.
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The objective of this study is to examine how the share of public transit investments affects urban structure using spatial-temporal distribution of transport passenger flows over transport network alternatives. A methodology to model urban structure (identifying and classifying centers and subcenters) based on urban travel data (interzonal urban passenger flows via urban rail modes during morning peak-hour) was developed using hierarchical cluster analysis for the case study of Istanbul Metropolitan City in Turkey since the rail investment is the major determinant in the definition of the network alternatives studied. Effects of the alternatives of Istanbul's 2023 Transport Master Plan networks on urban structure were modeled and compared using hierarchical cluster analyses (HCA). Analysis of the travel patterns over the alternative transport networks did not yield significant differences under the given constraint that the number of total trips in the metropolitan city was constant for all scenarios.
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Akin, Darcin, and Serdar Alasalvar. "Estimate Urban Growth and Expansion by Modeling Urban Spatial Structure Using Hierarchical Cluster Analyses of Interzonal Travel Data." In Megacities and Rapid Urbanization, 518–48. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9276-1.ch026.
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Estimating the spatial organization of cities yields insights into interactions over a spatial structure, and thus creating efficient subcenters with more balanced distribution of travel patterns over urban agglomerations can be exercised via models which support an evidence-based spatial planning. As cities evolve and self-organize as complex spatial structures, problems such as accessibility, environmental sustainability, and social equity or weak economy can be incurred by unrealistic development scenarios. In this regard, it is claimed that the dynamic nature of the urban spatial structure can to be modeled to estimate growth and expansion of it using the patterns of freight and passenger movements throughout metropolitan areas under the assumption that there is a simple and straightforward link between travel flows and urban spatial structure. The main effort of this study is to describe and model urban spatial structure and its evolution due to the spatial distribution of population, and employment centers.
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Akin, Darcin, and Serdar Alasalvar. "Modeling the Change of Urban Spatial Structure: Use Interzonal Travel Data to Estimate Urban Growth and Expansion by Hierarchical Cluster Analyses." In Advances in Civil and Industrial Engineering, 168–202. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8648-9.ch007.
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The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban spatial structure can be modeled using interzonal (O/D) travel data. Thus, the chapter presents a case study of modeling spatial structures developed by employing Hierarchical Cluster Analysis (HCA) using travel pattern data for current and future scenarios. As a result, urban growth and expansion were estimated based on the level of interaction (represented by distance or similarity modeled based on trip interchanges) over the transportation system in terms of population and/or employment increases. The interaction was described by a measure of distance or similarity, modeled with respect to trip interchanges.
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De Blij, Harm. "Geography of Jeopardy." In The Power of Place. Oxford University Press, 2008. http://dx.doi.org/10.1093/oso/9780195367706.003.0009.
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Everyone lives with risk, every day. In the United States, more than 100,000 persons die from accidents every year, nearly half of them on the country’s roads. Worldwide, an average of more than 5000 coal miners perish underground annually, a toll often forgotten by those who oppose nuclear power generation on grounds of safety. From insect bites to poisoned foods and from smoking to travel, risk is unavoidable. Certain risks can be mitigated through behavior (not smoking, wearing seatbelts), but others are routinely accepted as inescapable. A half century ago, long before hijackings and airport security programs, the number of airline travelers continued to increase robustly even as airplanes crashed with considerable frequency. Today, few drivers or passengers are deterred by the carnage on the world’s roads, aware of it though they may be. Risk is part of life. Risk, however, also is a matter of abode, of location. Who, after experiencing or witnessing on television the impact of a hurricane, a tornado, an earthquake, a volcanic eruption, a flood, a blizzard, or some other extreme natural event, has not asked the question: “Where in the world might be a relatively safe place to live?” Geographers, some of whom have made the study of natural hazards and their uneven distribution a research priority, don’t have a simple answer. But on one point they leave no doubt: people, whether individually or in aggregate, subject themselves to known environmental dangers even if they have the wherewithal to avoid them. Many Americans build their retirement or second homes on flood-prone barrier islands along coastlines vulnerable to hurricanes. The Dutch, who have for many years been emigrating from the Netherlands in substantial numbers, are leaving for reasons other than the fact that two-thirds of their country lies below sea level. From Indonesia to Mexico, farmers living on the fertile slopes of active volcanoes not only stay where they are, but often resist even temporary relocation when volcanic activity resumes. From Tokyo to Tehran, people continue to cluster in cities with histories of devastating earthquakes and known to be situated in perilous fault zones. Fatalism is a cross-cultural human trait.
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Chiappa, Giacomo Del, Francesca Checchinato, and Marcello Atzeni. "Residents’ perceptions of cruise tourism in an overcrowded city The case of Venice." In Sustainable and Collaborative Tourism in a Digital World. Goodfellow Publishers, 2021. http://dx.doi.org/10.23912/9781911635765-4834.
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Tourism is one of the most important industries in Europe: it represents 10% of the European Union GDP and 12 million people are employed in this sector (UNWTO, 2018). Due to its important contribution to the economy and its impact on the community, it affects the everyday life of residents, both in a positive and negative way. Within the industry, cruise tourism is the fastest growing segment of leisure tourism (Klein, 2011). In the last twenty years, the cruise sector has increased significantly, amounting to 24.7 million passengers in 2016 (CLIA, 2018) and employing 1,021,681 people around the world (BREA, 2017). Further, the cruise sector produces $57.9 billion in direct expenditures, thus creating a total economic output of $125.96 billion worldwide. In this scenario, academic research has devoted to analyze the residents’ perceptions and attitudes toward cruise tourism development (i.e. Brida et al., 2011; Del Chiappa & Abbate, 2016). However, studies have mostly analyzed cruising destinations in the Caribbean, Arctic and the polar areas (Hritz & Cecil 2008; Diedrich 2010; Klein 2010; Stewart et al., 2013; Heeney, 2015; Stewart et al., 2015; Jordan & Vogt, 2017) and, recently, also in the Mediterranean area (Marušić et al., 2008; Brida et al., 2012; Peručić & Puh, 2012; Pulina et al., 2013; Del Chiappa & Abbate 2016; Del Chiappa et al., 2017; Del Chiappa, et al., 2018b; Del Chiappa, et al., 2018c), mainly focusing on port-of-call cruise destinations. Despite this, only few studies have been carried out on homeport cruise destinations so far (Brida & Zapata 2010), and very few studies exist on destinations where the number of tourists creates massive overcrowding. This study was therefore carried out by surveying a quota sample of 354 individuals residing in Venice. Venice was selected as the research setting for this study for two main reasons. First, it is the second homeport in the Mediterranean area and one of the most famous tourism destinations worldwide, with around 24 million tourists a year. Second, it is considered to be affected by the so-called overtourism phenomenon (Seraphin et al., 2018). Anti-tourism movements have been growing in the last few years, voicing their concerns toward the continuous growth of the tourism phenomenon in the city, particularly toward cruise-related activities. This renders the research setting particularly interesting for the purposes of this study. Specifically, this paper aims to profile residents in Venice according to their perceptions towards the impacts of cruise tourism, and to ascertain whether there are significant differences among the clusters based on the socio-demographic traits of respondents.
Conference papers on the topic "Passenger clusters"
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Sperry, Ben, and Curtis Morgan. "Case Study of Cluster Analysis in Intercity Passenger Rail Planning and Marketing." In 2011 Joint Rail Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/jrc2011-56025.
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Recent policy and regulatory initiatives have established new momentum for intercity passenger rail among planners, policymakers, and the general public. As a result, there is a great interest in developing new passenger rail lines and expanding existing routes in intercity corridors across the country. Moving forward, there exists a need to understand how current passenger rail services are being utilized, who is riding them, and what changes could be implemented to existing routes to attract ridership — as well as to document lessons learned from existing lines that can aid service development planning for newly proposed routes. In this paper, cluster analysis is applied to passenger survey data obtained in 2007 from riders of three Amtrak routes in the state of Michigan, USA. Cluster analysis is a multivariate data analysis method used extensively in marketing and customer profile research which seeks to identify similarities among potential customers that are not immediately evident using traditional grouping techniques. Data used in the formation of the passenger clusters include traveler alternatives to the passenger rail service and the importance of service attributes, on-board activities, and station amenities. These variables and other data from the passenger survey are then used to characterize the identified clusters in terms of what kinds of passengers are in each cluster and how these passengers benefit from the rail service. The passenger clusters are also analyzed for their potential response to service improvements such as reduced travel time, increased service frequencies, or improved intermodal connections. The findings of this case study can be applied in a number of activities related to intercity passenger rail service planning for existing as well as proposed routes. The findings provide valuable insight into the needs and preferences of current passengers and can be used to formulate strategies for equipment investments or the development of new on-board amenities. From a policy perspective, passengers’ preferences for alternative travel modes in the absence of the rail service reveal how the rail service supports intercity mobility for each of the clusters. Finally, from the cluster profile, potential strategies to attract new riders can be identified. The results show that clustering analysis methodology applied in this case study is a valuable tool for intercity passenger rail planning.
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Bychkova, A. A. "Autocorrelation analysis of passenger traffic at railway terminals in the cities of the Sverdlovsk region." In VIII Information school of a young scientist. Central Scientific Library of the Urals Branch of the Russian Academy of Sciences, 2020. http://dx.doi.org/10.32460/ishmu-2020-8-0020.
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The article considers the method of determining the spatial autocorrelation and clustering railway stations in the cities of Sverdlovsk region. The data of the passenger flow of the Railways for 2019 have been analyzed. Groups of cities that form clusters based on similar passenger transport results have been identified.
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Jordon, J. B., and L. Wang. "Monotonic and Cyclic Characterization of Five Different Casting Processes on a Common Magnesium Alloy." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50173.
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The monotonic and cyclic behavior of five different casting processes for AZ91 magnesium alloy is evaluated through microstructure characterization and mechanical testing. A passenger car control arm was cast by squeeze cast, low pressure permanent mold, low pressure permanent mold-electricmagnetic-pump, T-mag, and ablation processes. Samples were cut from twelve locations of the control arm for microstructure characterization. The grain size, porosity fraction, and porosity size were measured via optical microscopy. Different types and sizes of defects were present in each type of casting processes. The mechanical behavior characterization included monotonic tension, and fully-reversed fatigue tests. Sources of fatigue crack initiation were quantified using scanning electron microscopy. For both monotonic and cyclic loading conditions, poor mechanical performance was directly linked to the presence of large pores, oxide films, and/or pore shrinkage clusters.
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Dahal, Beema, S. M. Naziur Mahmud, and Debakanta Mishra. "Simulating Ballast Breakage Under Repeated Loading Using the Discrete Element Method." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6117.
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The ballast layer in a railroad track helps distribute loads from the superstructure to the formation; a well-designed ballast layer is also meant to prevent excessive vertical, lateral and longitudinal movement of the track under loading. When subjected to repeated loading, the granular ballast particles often undergo breakage leading to significant changes in the shear strength as well as drainage characteristics of the ballast layer. Excessive ballast degradation leads to increased vertical settlements, and is often associated with speed restrictions and increased passenger discomfort. Several researchers in the past have studied the phenomenon of ballast breakage in a laboratory setting. However, due to complexities associated with these large-scale laboratory tests, detailed parametric studies are often not feasible. In such cases, numerical modeling tools such as the Discrete Element Method (DEM) become particularly useful. This paper presents findings from an ongoing research effort at Boise State University aimed at studying the phenomenon of ballast breakage under repeated loading using a commercially available Discrete Element Package (PFC3D®). Ballast particles were simulated as clusters of balls bonded together, and were allowed to undergo breakage when either the maximum tensile stress or the maximum shear stress exceeded the corresponding bond strength value. Different factors studied during the parametric analysis were: (1) load amplitude; (2) loading frequency; (3) number of cycles of loading; (4) bond strength; and (5) particle size distribution. The objective was to identify the relative importance of different factors that govern the permanent deformation behavior of railroad tracks under loading.
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Diez de los Rios Mesa, Francisco Javier, Rocío De Oña López, and Juan De Oña López. "The effect of service attributes’ hierarchy on passengers’ segmentation. A light rail transit service case study." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3844.
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Market segmentation can help transit operators to identify groups of passengers that share particular characteristics and specific needs and requirements about the service. Traditionally, socioeconomic variables have been used to perform a simple segmentation, although satisfaction rates about service attributes were not similar among individuals belonging to a group. Cluster analysis emerges as a novel analytical technique for extracting passengers’ profiles. This paper investigates passengers’ profiles at the metropolitan Light Rail Transit service of Seville (Spain). Latent Class Clustering algorithm is applied and satisfaction rates about different service quality attributes are considered for the segmentation. Particularly, two different cluster analyses are accomplished: first level, with only socioeconomic attributes; and second level, with eight service quality factors and socioeconomic attributes. The service quality factors are obtained through a principal component analysis, at which, the large number of attributes describing the service is reduced into constructs underlying them. Equivalent satisfaction rates are calculated for these service factors. Then, homogeneous groups of passengers are obtained. Additionally, the main differences among cluster are identified.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3844
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Jiang, Manhua, Xiaopeng Fan, Fan Zhang, Chengzhong Xu, Haixia Mao, and Renkai Liu. "Characterizing On-Bus WiFi Passenger Behaviors by Approximate Search and Cluster Analysis." In 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE, 2016. http://dx.doi.org/10.1109/ccbd.2016.017.
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Ren, Gang, Xing Zhu, and Qi Cao. "Passenger Flow Estimation of Urban Rail Transit Section Based on Cluster Analysis." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.010.
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Bozdog, Nicolae Vladimir, Marc X. Makkes, Aart van Halteren, and Henri Bal. "RideMatcher: Peer-to-Peer Matching of Passengers for Efficient Ridesharing." In 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2018. http://dx.doi.org/10.1109/ccgrid.2018.00041.
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ZHANG, TING, RUI DING, YUAN-HONG QIU, YI-MING DU, TAO ZHOU, and YI-LIN ZHANG. "RESEARCH ON THE LEVEL OF URBAN ECONOMIC DEVELOPMENT IN SICHUAN PROVINCE." In 2021 International Conference on Management, Economics, Business and Information Technology. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/dtem/mebit2021/35647.
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This paper selects 21 cities and prefectures of Sichuan Province as the research object, adopts 12 comprehensive indicators, establishes a linear model through correlation analysis, carries out regression analysis to modify the model, and uses factor analysis and cluster analysis to study the level of urban economic development. The results show that the GDP of all regions in Sichuan Province has a strong correlation with the three indicators of total retail sales of social consumer goods, passenger volume, and urbanization rate, and plays a positive role in promoting the economic development of all cities in Sichuan Province. Chengdu ranks the highest in the comprehensive ranking of urban economic development level in Sichuan Province.
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Wang, Jingjing, Xi Chen, Zhihong Chen, and Lizeng Mao. "Cluster algorithm based on LDA model for public transport passengers' trip purpose identification in specific area." In 2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2016. http://dx.doi.org/10.1109/icite.2016.7581331.
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