Cruise tourism demand forecasting - the case of Dubrovnik

2013 ◽  
Vol 19 (1) ◽  
pp. 125-142
Author(s):  
Ivana Pavlić
Keyword(s):  
Local Community ◽  
Arima Model ◽  
Demand Forecasting ◽  
Management Policy ◽  
Tourism Demand ◽  
Average Growth ◽  
Cruise Ships ◽  
Short Period ◽  
Cruise Tourism ◽  
The Mediterranean

The purpose – Cruising is nowadays a mass phenomenon since an increasing number of passengers worldwide have been taking part in this form of tourism. Therefore the purpose of this paper is to forecast cruise tourism demand at the level of micro destination. Design/Methodology/Approach – Dubrovnik has become one of the most important and most frequently visited destinations for cruise tourism in the Mediterranean. The rapidly increasing number of passengers on cruise voyages has put Dubrovnik among the leading cruise destinations in the Mediterranean. Dubrovnik is now facing the problem of concentration of a large number of ships and passengers in a short period of time. Consequently, this paper aims at forecasting the number of passengers from cruise ships within the next five year period in order to highlight eventual consequences and the necessity for implementation of a different management policy in accepting cruise ships and passengers at the destination to satisfy the requirements of both the passengers from cruisers and stationary tourists on one side and on the other side to improve the living standards of the local community. For this purpose the seasonal ARIMA model has been used which incorporates both seasonal autoregressive and moving average factor in the modelling process. Findings – With application of the above mentioned model and having in mind that forecasting was carried out under assumption that there will be no significant changes in the existing conditions it is to be concluded the cruise ship passenger arrivals in Dubrovnik area in 2015 will reach 1.294.316 making an increase of 31% in comparison with the year 2011 at an average growth rate of 7.06%. Originality of the research – Research was carried out to indicate the necessity for implementation of the new model of management for passengers from cruise ships by tourism destination management.

Smart Tourist Destination Management Using Demand Forecasting Techniques

2020 ◽  
pp. 273-293
Author(s):  
Leo Mrsic ◽  
Gorazd Surla ◽  
Mislav Balkovic
Keyword(s):  
Growth Phase ◽  
Management System ◽  
Demand Forecasting ◽  
Early Growth ◽  
Growth Potential ◽  
Tourism Demand ◽  
Demand And Supply ◽  
Short Period ◽  
Sustainability Issue ◽  
Early Growth Phase

Tourism destination is the place where tourism demand and supply meet. Destination is often the main reason why people travel. In the first part of the chapter, the research is focused on the fast growth of tourism in the past decade, which does not come without problems. As one of the most discussed problems, the focus is on overtourism, which started as growth potential and moved into a sustainability issue. The scope of this chapter was to continue previous conducted research in the Croatian coastal town Šibenik and build additional scenarios using technological advancements and available data to build a cornerstone for a data-driven Destination Management System. The results of two experiments suggest that usage of simple techniques can be widely adopted in the search for sustainable management of the destination. In the second part of the research, the authors were able to combine data before outbreak of Coronavirus COVID-19 and during its early growth phase in Croatia to show what devastating impact it has on tourist arrivals in a short period of time and demonstrate in real time how fragile tourism demand is.

scholarly journals Archeological tourism as a segment of cruise tourism offer on the example of Roman mosaics sites in Risan in the Bay of Kotor

2019 ◽  
Vol 135 ◽  
pp. 03076
Author(s):  
Goran Radovic ◽  
Nikola Konjevic
Keyword(s):  
Cultural Tourism ◽  
Tourist Destinations ◽  
Cruise Ships ◽  
Roman Villa ◽  
Floor Mosaics ◽  
Cruise Tourism ◽  
The Mediterranean ◽  
Culture Tradition ◽  
Archeological Site

Cruise tourism, most often, is related to the landing of a ship in one or more ports, i.e. tourist destinations, in which passengers and crew go ashore. In the richness of the offer that a port in the Adriatic and the Mediterranean offers to cruise tourists is certainly the part that can be called: culture, tradition, monuments, by which the areas on the map of world cruises are recognized. The port of Kotor, which is the main port for the reception of cruise ships in Montenegro, has for years achieved significant results in the reception and dispatch of cruise ships. Thus, through the port of Kotor, during 2018, 412 ships carrying 492, 475 passengers visited Montenegro. The paper, through the example of the Roman Mosaic site in Risan in the Bay of Boka Kotorska, analyses the effects of organized visits by tourists from cruise ships arriving to Kotor, and the importance and value of archeological sites in tourist cruising offer and the interaction between business and culture. The archeological site in Risan with the remains of the Roman Villa Urbana with preserved floor mosaics from the 2nd century represents a significant and attractive segment in the offer and development of archeological tourism as a subset of cultural tourism.

scholarly journals Port Governance and Cruise Tourism

2021 ◽  
Vol 13 (9) ◽  
pp. 4877
Author(s):  
Alejandro Vega-Muñoz ◽  
Guido Salazar-Sepúlveda ◽  
Nicolás Contreras-Barraza ◽  
Lorena Araya-Silva
Keyword(s):  
Core Collection ◽  
Web Of Science ◽  
Selection Process ◽  
Meta Analysis ◽  
Eligibility Criteria ◽  
The Core ◽  
Cruise Ships ◽  
Governance Model ◽  
Cruise Tourism ◽  
Governance Models

Cruise activities, until 2020, have presented a significant increase in revenue, as well as number of cruises and passengers transported, and it has become a challenge for ports to respond to this demand for services. In response to this, the world’s ports have implemented different governance models. In this context, in this paper, we aim to review the different governance models, as well as port cooperation, competition, and stakeholders. For this purpose, using science metric meta-analysis, an article set is extracted that strictly refers to the governance model of two databases integrated into the Core Collection Web of Science, whose selection process is polished with the PRISMA guidelines, establishing the eligibility criteria of studies using PICOS tool, to which a qualitative meta-analysis is applied. A limited studies set is identified, that includes governance model implementations, private strategies and internalization patterns in the port sector and cruise ships, patterns of port cooperation and governance, governance models in cruise ports, structures and strategies, and changes in the cruise market. Finally, various governance model forms are determined, all documented in the scientific research worldwide, discussing the various components of study topics.

scholarly journals Tourism Demand Forecasting Based on an LSTM Network and Its Variants

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 243
Author(s):  
Shun-Chieh Hsieh
Keyword(s):  
Adaptive Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Series Data ◽  
Learning Approaches ◽  
Tourism Demand ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Gated Recurrent Unit

The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002–2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.

Pooling in Tourism Demand Forecasting

2018 ◽  
Vol 58 (7) ◽  
pp. 1161-1174 ◽  
Author(s):  
Wen Long ◽  
Chang Liu ◽  
Haiyan Song
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Demand Forecasting ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Spatial Effects ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Demand Models ◽  
Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

scholarly journals Forecasting tomorrow’s tourist

2016 ◽  
Vol 8 (6) ◽  
pp. 643-653 ◽  
Author(s):  
Sérgio Moro ◽  
Paulo Rita
Keyword(s):  
Literature Review ◽  
Design Methodology ◽  
Scientific Literature ◽  
Recent Literature ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
Content Type ◽  
Current Trends ◽  
Modeling Techniques ◽  
Excellent Source

Purpose This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.

Tourism Demand Forecasting Using Nonadditive Forecast Combinations

2021 ◽  
pp. 109634802110478
Author(s):  
Yi-Chung Hu ◽  
Geng Wu ◽  
Peng Jiang
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Combination Method ◽  
Fuzzy Integral ◽  
Tourism Demand ◽  
Grey Prediction ◽  
Investment Planning ◽  
Single Model ◽  
Combining Forecasts ◽  
Forecast Combinations

Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.

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