Search results

This paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).

By analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.

According to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.

This study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.

This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.

The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).

A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.

High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.

This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

The purpose of this paper is to examine visitor management in the German-Swiss border area of the Lake Constance region. Taking a customer perspective, it determines the requirements for an application with the ability to optimize personal mobility.

A quantitative study and a survey of focus groups were conducted to identify movement patterns of different types of visitors and their requirements concerning the development of a visitor management application.

Visitors want an application that provides real-time forecasts of issues such as traffic, parking and queues and, at the same time, enables them to create a personal activity schedule based on this information.

Not every subsample reached a sufficient number of cases to yield representative results.

The results may lead to an optimization and management separation of mobility flows in the research area and be helpful to municipal planners, destination marketing organizations and visitors.

The German border cities of Konstanz, Radolfzell and Singen in the Lake Constance region need improved visitor management, mainly because of a high level of shopping tourism by Swiss visitors to Germany. In the Summer months, Lake Constance is also a popular destination for leisure tourists, which causes overtourism. For the first time, the results of this research presented here offer possible solutions, in particular by showing how a mobile application for visitors could defuse the situation.

The purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.

A Lean Manufacturing tool, the 5-Whys Analysis, has been used to find out the root causes associated with the problem identified from a data analysis of production orders of a fashion-luxury company. A case study, which explains the methodology and illustrates the capability of the tool, is provided.

This tool can be considered a suitable instrument to identify the causal factors of inefficiencies within luxury supply chains, suggesting potential countermeasures able to eliminate the problems previously highlighted. In addition, enabling technologies that deal with Industry 4.0 are associated with the root causes to enable further improvement of the supply chain.

The effectiveness and practicality of the tool are illustrated using an industrial case study concerning an international Italian signature in the world of fashion-luxury footwear sector.

This framework provides practitioners with an operative tool useful to highlight where the major inefficiencies of fashion-luxury supply chains take place and, at the same time, individuates both the root causes of inefficiencies and the corresponding corrective actions, even considering Industry 4.0 enabling technologies.

The purpose of this paper is to adopt the uses and gratifications theory to tourism.

This paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.

Both a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.

The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.

The focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017.

To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments.

In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries.

Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings.

One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

The purpose of this paper is to deliver a detailed analysis of the functioning of upstream–downstream interface process of the tsunami early warning and mitigation system in Sri Lanka. It also gives an understanding of the social, administrative, political and cultural complexities attached to the operation of interface mechanism, and introduces an analytical framework highlighting the significant dynamics of the interface of tsunami early warning system in Sri Lanka.

Through the initial literature review, a conceptual framework was developed, highlighting the criteria against which the interface process can be assessed. This framework was used as the basis for developing data collection tools, namely, documentary analysis, semi-structured interviews and observations that focused on the key stakeholder institutions in Sri Lanka. Thematic analysis was used to analyze the data according to the conceptual framework, and an improved and detailed framework was developed deriving from the findings.

The manner in which the interface mechanism operates in Sri Lanka’s tsunami early warning system is discussed, providing a detailed understanding of the decision-making structures; key actors; standardisation; technical and human capacities; socio-spatial dynamics; coordination among actors; communication and information dissemination; and the evaluation processes. Several gaps and shortcomings were identified with relation to some of these aspects, and the significance of addressing these gaps is highlighted in the paper.

A number of recommendations are provided to address the existing shortcomings and to improve the overall performance of tsunami warning system in Sri Lanka.

Based on the findings, a framework was developed into a more detailed analytical framework that depicts the interface operationalisation in Sri Lanka, and can also be potentially applied to similar cases across the world. The new analytical framework was validated through a focus group discussion held in Sri Lanka with the participation of experts and practitioners.

Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics.

The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools.

Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace.

The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.