Search results for: travel demand modelling

Authors: Raja Al-Khalili

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Traveling abroad for British citizens in the eighteenth century was usually performed for two reasons. The first major form was for administering the expanding realm of the British Empire and its need for officials in governing the natives and facilitating the work of business companies. The other form of travel was for pleasure and involved a manifestation of wealth. This form of travel was a prelude for the modern industry of tourism and usually involved a tour of Europe and the Mediterranean. In both forms of travel the British encountered a myriad of cultures. Travel had fostered a sense of pride and confirmed an ethnocentric view of British superiority, but it also brought a critical self-examination of belonging to a colonial empire that thrives on the weaknesses of other nations. Women writers in particular have sought in the travels a kind of self-exploration of the nature of social patriarchy in a diversity of cultures. Both Lady Elizabeth Craven in A Journey through the Crimea to Constantinople (1789) and Lady Mary Wortley Montagu in Embassy Letters (1716-1718) have observed the culture of the Ottomans and then pursued to reflect on the social role of women in England.

Keywords: travel writing, Elizabeth Craven, Lady Mary Wortley, patriarchy

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Siripen Yiamjanya, Kevin Wongleedee

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This research paper aims to identify travel motivation by push and pull factors that affected decision making of international tourists in selecting Thailand as their destination choice. A total of 200 international tourists who traveled to Thailand during January and February, 2014 were used as the sample in this study. A questionnaire was employed as a tool in collecting the data, conducted in Bangkok. The list consisted of 30 attributes representing both psychological factors as “push- based factors” and destination factors as “pull-based factors”. Mean and standard deviation were used in order to find the top ten travel motives that were important determinants in the respondents’ decision making process to select Thailand as their destination choice. The finding revealed the top ten travel motivations influencing international tourists to select Thailand as their destination choice included [i] getting experience in foreign land; [ii] Thai food; [iii] learning new culture; [iv] relaxing in foreign land; [v] wanting to learn new things; [vi] being interested in Thai culture, and traditional markets; [vii] escaping from same daily life; [viii] enjoying activities; [ix] adventure; and [x] good weather. Classification of push- based and pull- based motives suggested that getting experience in foreign land was the most important push motive for international tourists to travel, while Thai food portrayed its highest significance as pull motive. Discussion and suggestions were also made for tourism industry of Thailand.

Keywords: decision making, destination choice, international tourist, pull factor, push factor, Thailand, travel motivation

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Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

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Although older drivers (defined as those of age 65 and above) are less involved with speeding, alcohol use as well as night driving, they are more vulnerable to severe crashes. The major contributing factors for severe crashes include frailty and medical complications. Several studies have evaluated the contributing factors on severity of crashes. However, few studies have established the impact of travel time reliability (TTR) on road safety. In particular, the impact of TTR on senior adults who face several challenges including hearing difficulties, decreasing of the processing skills and cognitive problems in driving is not well established. Therefore, this study focuses on determining possible impacts of TTR on the traffic safety with focus on elderly drivers. Historical travel speed data from freeway links in the study area were used to calculate travel time and the associated TTR metrics that is, planning time index, the buffer index, the standard deviation of the travel time and the probability of congestion. Four-year information on crashes occurring on these freeway links was acquired. The binary logit model estimated using the Markov Chain Monte Carlo (MCMC) sampling technique was used to evaluate variables that could be influencing elderly crash severity. Preliminary results of the analysis suggest that TTR is statistically significant in affecting the severity of a crash involving an elderly driver. The result suggests that one unit increase in the probability of congestion reduces the likelihood of the elderly severe crash by nearly 22%. These findings will enhance the understanding of TTR and its impact on the elderly crash severity.

Keywords: highway safety, travel time reliability, elderly drivers, traffic modeling

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Authors: Kaiping Xu, Aiju You, Lei Hua

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In view of the ecological environmental problems and protection needs of mountainous rivers in Zhejiang province, a suitable ecological water demand evaluation system was established based on investigation and monitoring. Taking the Kaihua river as an example, the research on ecological water demand and the current situation evaluation were carried out. The main types of ecological water demand in Majin River are basic ecological flow and lake wetland outside the river, and instream flow and water demands for water quality in Zhongcun river. In the wet season, each ecological water demand is 18.05m3/s and 2.56m3 / s, and in the dry season is 3.00m3/s and 0.61m3/s. Three indexes of flow, duration and occurrence time are used to evaluate the ecological water demand. The degree of ecological water demand in the past three years is low level of satisfaction. Meanwhile, the existing problems are analyzed, and put forward reasonable and operable safeguards and suggestions.

Keywords: Zhejiang province, mountainous river, ecological water demand, Kaihua river, evaluation

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Authors: Selvakumar Ulaganathan, Ivo Couckuyt, Francesco Ferranti, Tom Dhaene, Eric Laermans

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Variable-fidelity surrogate modelling offers an efficient way to approximate function data available in multiple degrees of accuracy each with varying computational cost. In this paper, a Kriging-based variable-fidelity surrogate modelling approach is introduced to approximate such deterministic data. Initially, individual Kriging surrogate models, which are enhanced with gradient data of different degrees of accuracy, are constructed. Then these Gradient enhanced Kriging surrogate models are strategically coupled using a recursive CoKriging formulation to provide an accurate surrogate model for the highest fidelity data. While, intuitively, gradient data is useful to enhance the accuracy of surrogate models, the primary motivation behind this work is to investigate if it is also worthwhile incorporating gradient data of varying degrees of accuracy.

Keywords: Kriging, CoKriging, Surrogate modelling, Variable- fidelity modelling, Gradients

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Authors: Roghieh Eskandari, Ali Motamedi

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There is a significant demand for renovation as-built assets are aging. To plan for a desirable and comfortable indoor environment, stakeholders use simulation technics to assess potential renovation scenarios with the innovative designs. Diminished Reality (DR), which is a technique of visually removing unwanted objects from the real-world scene in real-time, can contribute to the renovation design visualization for stakeholders by removing existing structures and assets from the scene. Using DR, the objects to be demolished or changed will be visually removed from the scene for a better understanding of the intended design scenarios for stakeholders. This research proposes an integrated system for renovation plan visualization using Building Information Modelling (BIM) data and mixed reality (MR) technologies. It presents a BIM-based DR method that utilizes a textured BIM model of the environment to accurately register the virtual model of the occluded background to the physical world in real-time. This system can facilitate the simulation of the renovation plan by visually diminishing building elements in an indoor environment.

Keywords: diminished reality, building information modelling, mixed reality, stock renovation

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Authors: Avrokomi Zavitsanou, Spiros Papadopoulos, Theofanis Alexandridis

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This paper presents the research project ‘Colour & Travel’, which is co-funded by the European Union and national resources through the Operational Programme “Competitiveness, Entrepreneurship and Innovation” 2014-2020, under the Single RTDI State Aid Action "RESEARCH - CREATE - INNOVATE". The research project proposes the design of an innovative, playful framework for exploring a variety of travel destinations and creating personalised travel narratives, aiming to entertain, educate, and promote culture and tourism. Gamification of the cultural and touristic environment can enhance its experiential, multi-sensory aspects and broaden the perception of the traveler. The latter's involvement in creating and shaping his personal travel narrations and the possibility of sharing it with others can offer him an alternative, more binding way of getting acquainted with a place. In particular, the paper presents the design of an infrastructure: (a) for the development of interactive travel guides for mobile devices, where sites with specific points of interest will be recommended, with which the user can interact in playful ways and then create his personal travel narratives, (b) for the development of innovative games within virtual reality environment, where the interaction will be offered while the user is moving within the virtual environment; and (c) for an online application where the content will be offered through the browser and the modern 3D imaging technologies (WebGL). The technological products that will be developed within the proposed project can strengthen important sectors of economic and social life, such as trade, tourism, exploitation and promotion of the cultural environment, creative industries, etc. The final applications delivered at the end of the project will guarantee an improved level of service for visitors and will be a useful tool for content creators with increased adaptability, expansibility, and applicability in many regions of Greece and abroad. This paper aims to present the research project by referencing the state of the art and the methodological scheme, ending with a brief reflection on the expected outcome in terms of results.

Keywords: gamification, culture, tourism, AR, VR, applications

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Authors: Banan Aljumaiah

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In recent years, the construction industry has leveraged the information revolution, which makes it possible to view the entire construction process of new buildings before they are built with the advent of Building Information Modelling (BIM). Although BIM is an integration of the building model with the data and documents about the building, however, its implementation is limited to individual buildings missing the large picture of the city infrastructure. This limitation of BIM led to the birth of City Information Modelling. Three years ago, Eastern Province Municipality (EPM) in Saudi Arabia mandated that all major projects be delivered with collaborative 3D BIM. After three years of implementation, EPM started to implement City Information Modelling (CIM) as a part of the Smart City Plan to link infrastructure and public services and modelling how people move around and interact with the city. This paper demonstrates a local case study of BIM implementation in EPM and its future as a part of project management automation; the paper also highlights the ambitious plan of EPM to transform CIM towards building smart cities.

Keywords: BIM, BIM to CIM

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Authors: Vittorio Caffi, Maria Pignataro, Antonio Cosimo Devito, Marco Pesenti

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This paper deals with the integration of advanced digital design and modelling tools and methodologies, known as Building Information Modelling, into the traditional Academy educational programs for building engineers and architects. Nowadays, the challenge the Academy has to face is to present the new tools and their features to the pupils, making sure they acquire the proper skills in order to leverage the potential they offer also for the other courses embedded in the educational curriculum. The syllabus here presented refers to the “Drawing for building engineering”, “2D and 3D laboratory” and “3D modelling” curricula of the MSc in Building Engineering of the Politecnico di Milano. Such topics, included since the first year in the MSc program, are fundamental to give the students the instruments to master the complexity of an architectural or building engineering project with digital tools, so as to represent it in its various forms.

Keywords: BIM, BIM curricula, computational design, digital modelling

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Authors: Vishnu S. Prasad, Preeti Aghalayam

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The major harmful automobile exhausts are nitric oxide (NO) and unburned hydrocarbon (HC). Reduction of NO using unburned fuel HC as a reductant is the technique used in hydrocarbon-selective catalytic reduction (HC-SCR). In this work, we study the microkinetic modelling of NO reduction using propene as a reductant on Pt catalysts. The selectivity of NO reduction to N₂O is detected in some ranges of operating conditions, whereas the effect of inlet O₂% causes a number of changes in the feasible regimes of operation.

Keywords: microkinetic modelling, NOx, platinum on alumina catalysts, selective catalytic reduction

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Authors: Agarana Michael C., Akinlabi Esther T., Pule Kholopane

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Innovation in manufacturing process technologies and associated product design affects the prospects for manufacturing today and in near future. In this study some theoretical methods, useful as tools in advanced manufacturing, are considered. In particular, some basic Mathematical, Operational Research, Heuristic, and Statistical techniques are discussed. These techniques/methods are very handy in many areas of advanced manufacturing processes, including process planning optimization, modelling and analysis. Generally the production rate requires the application of Mathematical methods. The Emerging Trends Processes in Advanced Manufacturing can be enhanced by using Mathematical Modelling and Optimization techniques.

Keywords: mathematical modelling, optimization, emerging trends, advanced manufacturing

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Authors: Joao Ferreira Do Rosario, Nuno Gustavo, Ana Machado, Lurdes Calisto, Luisa Carvalho, Georgette Andraz

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Currently, tourism small and medium enterprises (TSMEs) face serious economic and financial problems, making recovery efforts difficult. How the pandemic will affect tourists' behavior is still to be known. Will tourists be even more cautious regarding their choices or, on the contrary, will they be more adventurers with an enormous desire to travel in search of the lost freedom? Tourists may become even more demanding when traveling, more austere, or less concerned and eager to socialize. Adjusting to this "new tourist" is an added challenge for tourism service providers. Generation Z made up of individuals born in 1995 and following years, currently tends to assume a particular role and meaning in the present and future economic and social context, considering that we are facing the youngest workforce as well as tomorrow's consumers. This generation is distinguished from others as it is the first generation to combine a high level of education and technological knowledge and to fully experience the digital world. These young people are framed by a new value system that can explain new behaviours and consumption, namely, in the context of tourism. All these considerations point to the importance of investigating this target group as it is essential to understand how these individuals perceive, understand, act, and can be involved in a new environment built around a society regulated by new priorities and challenges of a sustainable nature. This leads not only to a focus on short-term market choices but mainly to predict future choices from a longer-term perspective. Together with the social background of a person, values are considered a stable antecedent of behavior and might therefore predict not just immediate, but also future choices. Furthermore, the meaning attributed to travel has a general connotation and goes beyond a specific travel choice or experience. In other words, values and travel's meaning form a chain of influences on the present and future travel behavior. This study explores the social background and values of Generation Z travelers vs the meaning these tourists give to travel. The aim is to discover in their present behavior cues to predict travel choices so that the future of tourism can be secured. This study also provides data for predicting the tourism choices of youngsters in the more immediate future. Methodologically, a quantitative approach was adopted based on the collection of data through a survey. Since academic research on Generation Z of tourists is still scarce, it is expected to contribute to deepening scientific knowledge in this area. Furthermore, it is expected that this research will support tourism professionals in defining differentiated marketing strategies and adapted to the requirements of this target, in a new time.

Keywords: Generation Z, travel behavior, travel meaning, Generation Z Values

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Authors: Mahmud Hafsat Hussaini, Eldah Ephraim Eldah, Bata Zoakah Amina

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This research work was aimed at examining the challenges faced by passengers using Arik air for domestic and international travels. Passengers do complain of delay flights, theft and rude behavior by Arik staff while on transit or in the process of travelling using the aircraft. Being the national carrier in Nigeria these behaviors have tarnished the image of the airline and makes travel experience to be challenging. Hundred survey questionnaires were administered to travellers who have used the airline for domestic and international flights. Findings show that the staff of the airline do lack customer care skills and are sometimes rude to customers. The airline does have different agents that book for international flights who delays confirming bookings even after payment. The website of the airline is mostly down and makes bookings difficult. Other findings related to the study are a delay of domestic flights within Nigeria. Passengers are sometimes kept for 8 hours in the airport due to delay of flights. The study, therefore, recommends that flight schedule should be adhered to and staff should be trained to meet of with passengers demand. The security of guest luggage at the airport should be put in place to avoid theft. An effective booking platform should be accessible to passengers for easy booking.

Keywords: examining, challenges, domestic, international, travels

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Authors: Ran Etgar, Yuval Cohen, Erel Avineri

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One of the major challenges of transportation in medium sized inner-cities (such as Tel-Aviv) is the last-mile solution. Personal rapid transit (PRT) seems like an applicable candidate for this, as it combines the benefits of personal (car) travel with the operational benefits of transit. However, the investment required for large area PRT grid is significant and there is a need to economically justify such investment by correctly evaluating the grid capacity. PRT main elements are small automated vehicles (sometimes referred to as podcars) operating on a network of specially built guideways. The research is looking at a specific concept of elevated PRT system. Literature review has revealed the drawbacks PRT modelling and simulation approaches, mainly due to the lack of consideration of technical and operational features of the system (such as headways, acceleration, safety issues); the detailed design of infrastructure (guideways, stations, and docks); the stochastic and sessional characteristics of demand; and safety regulations – all of them have a strong effect on the system performance. A highly detailed model of the system, developed in this research, is applying a discrete event simulation combined with an agent-based approach, to represent the system elements and the podecars movement logic. Applying a case study approach, the simulation model is used to study the capacity of the system, the expected throughput of the system, the utilization, and the level of service (journey time, waiting time, etc.).

Keywords: capacity, productivity measurement, PRT, simulation, transportation

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Claudia Seabra, Elisabeth Kastenholz, José Luís Abrantes, Manuel Reis

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In a world of increasing mobility and global risks, terrorism has, in a perverse way, capitalized on contemporaneous society’s growing interest in travel to explore a world whose national boundaries and distances have decreased. Terrorists have identified the modern tourist flows originated from the economically more developed countries as new appealing targets so as to: i) call attention to the causes they defend and ii) destroy a country’s foundations of tourism, with the final aim of disrupting the economic and consequently social fabric of the affected countries. The present study analyses sensitivity towards risk and travel behaviors in international travel amongst a sample of 600 international tourists from 49 countries travelling by air. Specifically, the sample was segmented according to the Global Peace Index. This index defines country profiles regarding the levels of peace. The indicators used are established over three broad themes: i) ongoing domestic and international conflict; ii) societal safety and security; and iii) militarisation. Tourists were segmented, according to their country of origin, in different levels of peacefulness. Several facets of travel behavior were evaluated, namely motivations, attitude towards trip planning, quality perception and perceived value of the trip. Also factors related with risk perception were evaluated, specifically terrorism risk perception during the trip, unsafety sensation as well as importance attributed to safety in travel. Results contribute to our understanding of the role of previous exposure to the lack of peace and safety at home in the international tourists behaviors, which is further discussed in terms of tourism management and marketing implications which should particularly interest tourism services and destinations more affected by terrorism, war, political turmoil, crime and other safety risks.

Keywords: terrorism, tourism, safety, risk perception

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Mariko Nishikawa, Masaaki Yamanaka, Ayami Kondo

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Background: International visitors to Japan are at a risk of travel-related illnesses or injury that could result in hospitalization in a country where the language and customs are unique. Over twelve million international visitors came to Japan in 2015, and more are expected leading up to the Tokyo Olympics. One aspect of this is the potentially greater demand on healthcare services by foreign visitors. Nurses who take care of them have anxieties and concerns of their knowledge of the Japanese health system. Objectives: An effective distribution of travel-health information is vital for facilitating care for international visitors. Our research investigates whether a four-minute digital animation (Mari Info Japan), designed and developed by the authors and applied to a survey of 513 nurses who take care of foreigners daily, could clarify travel health procedures, reduce anxieties, while making it enjoyable to learn. Methodology: Respondents to a survey were divided into two groups. The intervention group watched Mari Info Japan. The control group read a standard guidebook. The participants were requested to fill a two-page questionnaire called Mari Meter-X, STAI-Y in English and mark a face scale, before and after the interventions. The questions dealt with knowledge of health promotion, the Japanese healthcare system, cultural concerns, anxieties, and attitudes in Japan. Data were collected from an intervention group (n=83) and control group (n=83) of nurses in a hospital, Japan for foreigners from February to March, 2016. We analyzed the data using Text Mining Studio for open-ended questions and JMP for statistical significance. Results: We found that the intervention group displayed more confidence and less anxiety to take care of foreign patients compared to the control group. The intervention group indicated a greater comfort after watching the animation. However, both groups were most likely to be concerned about language, the cost of medical expenses, informed consent, and choice of hospital. Conclusions: From the viewpoint of nurses, the provision of travel-health information by digital animation to international visitors to Japan was more effective than traditional methods as it helped them be better prepared to treat travel-related diseases and injury among international visitors. This study was registered number UMIN000020867. Funding: Grant–in-Aid for Challenging Exploratory Research 2010-2012 & 2014-16, Japanese Government.

Keywords: digital animation, health promotion, international visitor, Japan, nurse

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Authors: Kausik Halder, Nabendu Chaki, Ranjan Dasgupta

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We present a modelling framework that supports the engineering of early requirements specifications for design of learner centric dynamic Learning Management System. The framework is based on i* modelling tool and Means End Analysis, that adopts primitive concepts for modelling early requirements (such as actor, goal, and strategic dependency). We show how pedagogical and computational requirements for designing a learner centric Learning Management system can be adapted for the automatic early requirement engineering specifications. Finally, we presented a model on a Learner Quanta based adaptive Courseware. Our early requirement analysis shows that how means end analysis reveals gaps and inconsistencies in early requirements specifications that are by no means trivial to discover without the help of formal analysis tool.

Keywords: adaptive courseware, early requirement engineering, means end analysis, organizational modelling, requirement modelling

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Authors: Isha Rathore, Peeyush Jain, Elangovan Rajasekar

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The thermal capacity of the envelope impacts the cooling and heating demand of a building and modulates the peak electricity demand. This paper presents the thermal capacity tuning of a building envelope to minimize peak electricity demand for space cooling. We consider a 40 m² residential testbed located in Hyderabad, India (Composite Climate). An EnergyPlus model is validated using real-time data. A Parametric simulation framework for thermal capacity tuning is created using the Honeybee plugin. Diffusivity, Thickness, layer position, orientation and fenestration size of the exterior envelope are parametrized considering a five-layered wall system. A total of 1824 parametric runs are performed and the optimum wall configuration leading to minimum peak cooling demand is presented.

Keywords: thermal capacity, tuning, peak demand reduction, parametric analysis

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Authors: Ferinar Moaidi, Mahdi Moaidi

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Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state.

Keywords: distributed generation, genetic algorithm, microgrid, load modelling, loss reduction, voltage improvement

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Authors: A. S. Zgalei, B. T. Al-Mabrouk

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With a particularly significant growth rate observed in the Libyan commercial and residential buildings coupled with a growth in energy demand, solar desiccant evaporative cooling offers energy savings and promises a good sharing for sustainable buildings where the availability of solar radiation matches with the cooling load demand. The paper presents a short introduction for the desiccant systems. A mathematical model of a selected system has been developed and a simulation has been performed in order to investigate the system performance at different working conditions and an optimum design of the system structure is established. The results showed a technical feasibility of the system working under the Libyan climatic conditions with a reasonable COP at temperatures that can be obtained through the solar reactivation system. Discussion of the results and the recommendations for future work are proposed.

Keywords: computer program, solar desiccant wheel cooling, system modelling, simulation, technical feasibility

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Authors: Tayebeh Saghapour, Sara Moridpour, Russell George Thompson

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One of the common aims of transport policy makers is to switch people’s travel to active transport. For this purpose, a variety of transport goals and investments should be programmed to increase the propensity towards active transport mode choice. This paper aims to investigate whether built environment features in neighbourhoods could enhance the odds of active transportation. The present study introduces an index measuring public transport accessibility (PTAI), and a walkability index along with socioeconomic variables to investigate mode choice behaviour. Using travel behaviour data, an ordered logit regression model is applied to examine the impacts of explanatory variables on walking trips. The findings indicated that high rates of active travel are consistently associated with higher levels of walking and public transport accessibility.

Keywords: active transport, public transport accessibility, walkability, ordered logit model

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Authors: Punrapha Praditpong, Surangkana Pipatchokchaiyo

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The objectives of this study were to study the consumer behavior of the Baby boomers, the X & the Y generation towards Online Travel Agents in purchasing tourism-related products and services. The research methodology of this research used the quantitative study and the sample size consisted of 400 questionnaires in five districts of Bangkok. The data was analyzed by frequency, percentage, mean and SD. Moreover, all the hypotheses were tested by One-Way ANOVA and Pearson-Correlation statistics. The research findings were as follows: 1) There were significant effects to the purchasing decision making process towards purchasing tourism related products and services via OTAs; 2) There were different consumer behaviors from the Baby boomers, the X generation and the Y generation towards purchasing tourism related products and services via OTAs, which are explained in detail in finding. The research offers a discussion and presents some recommendations for the OTA websites.

Keywords: consumer behavior, online travel agent, x generations, y generations

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Authors: Zheng Yi

Abstract:

Among the attempts made worldwide to foster urban and transport sustainability, transit-oriented development certainly is one of the most successful. Residents' travel behavior is a concern in the researches about the impacts of transit-oriented development. The study takes 620 English journal papers in the core collection database of Web of Science as the study objects; the paper tries to map out the scientific knowledge mapping in the field and draw the basic conditions by co-citation analysis, co-word analysis, a total of citation network analysis and visualization techniques. This study teases out the research hotspots and evolution of the relationship between urban rail transit and resident's travel behavior from 1999 to 2019. According to the results of the analysis of the time-zone view and burst-detection, the paper discusses the trend of the next stage of international study. The results show that in the past 20 years, the research focuses on these keywords: land use, behavior, model, built environment, impact, travel behavior, walking, physical activity, smart card, big data, simulation, perception. According to different research contents, the key literature is further divided into these topics: the attributes of the built environment, land use, transportation network, transportation policies. The results of this paper can help to understand the related researches and achievements systematically. These results can also provide a reference for identifying the main challenges that relevant researches need to address in the future.

Keywords: urban rail transit, travel behavior, knowledge map, evolution of researches

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Authors: Richard Sogah

Abstract:

The significance of transport infrastructure investments for economic growth and development has been central to the World Bank’s strategy for poverty reduction. Among the conventional surface transport infrastructures, road infrastructure is significant in facilitating the movement of human capital goods and services. When transport projects (i.e., roads, super-highways) are implemented, they come along with some negative social values (costs), such as increased noise and air pollution for local residents living near these facilities, displaced individuals, etc. However, these projects also facilitate better utilization of existing capital stock and generate other observable benefits that can be easily quantified. For example, the improvement or construction of roads creates employment, stimulates revenue generation (toll), reduces vehicle operating costs and accidents, increases accessibility, trade expansion, safety improvement, etc. Aside from these benefits, travel time savings (TTSs) which are the major economic benefits of urban and inter-urban transport projects and therefore integral in the economic assessment of transport projects, are often overlooked and omitted when estimating the benefits of transport projects, especially in developing countries. The absence of current and reliable domestic travel data and the inability of replicated models from the developed world to capture the actual value of travel time savings due to the large unemployment, underemployment, and other labor-induced distortions has contributed to the failure to assign value to travel time savings when estimating the benefits of transport schemes in developing countries. This omission of the value of travel time savings from the benefits of transport projects in developing countries poses problems for investors and stakeholders to either accept or dismiss projects based on schemes that favor reduced vehicular operating costs and other parameters rather than those that ease congestion, increase average speed, facilitate walking and handloading, and thus save travel time. Given the complex reality in the estimation of the value of travel time savings and the presence of widespread informal labour activities in Sub-Saharan Africa, we construct a “nationally ranked distribution of time values” and estimate the value of travel time savings based on the area beneath the distribution. Compared with other approaches, our method captures both formal sector workers and individuals/people who work outside the formal sector and hence changes in their time allocation occur in the informal economy and household production activities. The dataset for the estimations is sourced from the World Bank, the International Labour Organization, etc.

Keywords: road infrastructure, transport projects, travel time savings, congestion, Sub-Sahara Africa

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Authors: Kisan Sarda, Bhavika Shingote

Abstract:

This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

Procedia PDF Downloads 353