31 resultados para Travel Motivations

em Digital Commons at Florida International University


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Little research has been completed regarding spring break motivations and behaviors of American students in foreign destinations, specifically in Mexico. This paper looks at push and pull motivations in relation to drug and alcohol consumption and findings indicate greater drug and alcohol use among those who selected “party reputation” and “to go wild” as travel motivations. Binge drinking, sexual activity, and drug use among students on spring break in Acapulco, Mexico were also analyzed and compared to past findings within the United States. Results suggest that students are involved in heavy alcohol consumption and significant drug use. Additionally, high rates of sexual activity occur on spring break and results suggest low condom use, placing students at higher risks for the contraction of sexually transmitted infections.

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Abstract Driven by the political and economic forces of cross-strait, Taiwan has become one of the major source markets for Hong Kong tourism industry since 1987. The major purposes of this study were to investigate the following factors (1) The influential factors of travel motivation, (2) The clusters of travel motivations, (3) The marketing segmentation of clusters of Taiwanese tourists to visit Hong Kong. Through ten travel agents, self-report surveys were distributed to collect data from 366 Taiwanese travelers. Hence, four push factors and six pull factors were identified as travel motivations through the factor analysis. Combined with the cluster analysis; five new groups were founded. Finally, five clusters which process unique profiles (location difference, visiting frequency, travel satisfaction, and destination loyalty) were addressed. The suggestions of developing effective market strategies to attract Taiwanese tourists to Hong Kong were also provided.

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The aim of this study is to analyze the perception of European destinations through the eyes of Indian Bollywood film viewers to determine how perception is influenced by what is viewed in films. Researchers surveyed Indian consumers and collected 670 usable surveys. European destinations were divided into top five and bottom five destinations for Indian tourists, and data was then compared to world tourism statistics. Results indicate differences in destination preference among Bollywood viewers and worldwide tourist trends. Findings indicate that prominently featuring a landscape within Bollywood films can significantly impact Indians’ perception on the destinations’ image. European countries frequently portrayed in films have higher marks on multiple perception categories than those not featured in blockbuster Bollywood films.

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The primary objectives of this study were to describe the relationship that U.S. hotel companies have with travel agency companies and to identify the determinants of a successful relationship.^ The unit of analysis was hotel companies operating in U.S. One hundred and three hotel companies contributed information for this research. The data were collected through a questionnaire developed from previous empirical studies and from interviews with hospitality management professors and hotel sales and marketing directors.^ Simple and multiple regression analyses indicated that in order to have successful relationships with travel agency companies, hotel companies should (a) show more commitment or dedication to working with the travel agents, (b) have more trust in the travel agents themselves, and (c) be less dependent on any one travel agent for their business.^ Additionally, the results suggest that hotel companies who coordinate activities with travel agents companies and who communicate with them in a timely, accurate, adequate, complete and credible manner have more successful interorganizational relationships than those who do not.^ Furthermore, hotel companies who share proprietary sales and any other information with travel agents reported better relationships. (Abstract shortened by UMI.) ^

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Women have traditionally not attained the position of secondary school principal as frequently as their male counterparts. Research in the field of motivation theory suggests the significance and impact of gender on career paths. Theorists and researchers believe that women face an orientation to life that is different than men. Some see the principalship as satisfying different values and working toward some psychological needs. ^ In this study, two hypotheses were used to explore whether substantive differences existed between men and women with regard to factors that motivate secondary school administrators to become principals in Miami-Dade County Public Schools. The administrators were surveyed during meetings held with them at Region meetings. Participants were asked to rate the motivation factors on a 7-point Likert Scale (1-strongly disagree to 7-strongly agree) and to rank the motivation factors (1-most important to 10-least important) that might discourage one from becoming a principal as well as those that might encourage one to retire from the principalship; and to identify motivation factors that most influenced their decision to become a school administrator. Two hundred twenty-six surveys were returned.^ Quantitative data were collected to measure differences between men and women with respect to their motivation to seek the principalship in terms of their ratings on the Motivation Factors Survey. Based upon a Factor Analysis, four factors (intrinsic satisfiers, extrinsic satisfiers, principalship challenges and job values) were identified. A MANOVA using the factors as dependent variables revealed that, as predicted by research and theory, women were significantly more likely than men to be motivated by intrinsic satisfiers and job values. In support of this finding, open-ended responses to the survey revealed that men found extrinsic satisfiers to be of greater importance than intrinsic satisfiers. ^

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Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

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This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

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International travel has significant implications on the study of architecture. This study analyzed ways in which undergraduate and graduate students benefited from the experience of international travel and study abroad. Taken from the perspective of 15 individuals who were currently or had been architecture students at the University of Miami and Florida International University or who were alumni of the University of Florida and Syracuse University, the research explored how international travel and study abroad enhanced their awareness and understanding of architecture, and how it complemented their architecture curricula. This study also addressed a more personal aspect of international travel in order to learn how the experience and exposure to foreign cultures had positively influenced the personal and professional development of the participants.^ Participants’ individual and two-person semi-structured interviews about study abroad experiences were electronically recorded and transcribed for analysis. A second interview was conducted with five of the participants to obtain feedback concerning the accuracy of the transcripts and the interpretation of the data. Sketch journals and design projects were also analyzed from five participants and used as data for the purposes of better understanding what these individuals learned and experienced as part of their study abroad.^ Findings indicated that study abroad experiences helped to broaden student understanding about architecture and urban development. These experiences also opened the possibilities of creative and professional expression. For many, this was the most important aspect of their education as architects because it heightened their interest in architecture. These individuals talked about how they had the opportunity to experience contemporary and ancient buildings that they had learned about in their history and design classes on their home campuses. In terms of personal and professional development, many of the participants remarked that they became more independent and self-reliant because of their study abroad experiences. They also displayed a sense of global awareness and were interested in the cultures of their host nations. The study abroad experiences also had a lasting influence on their professional development.^

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The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

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Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

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Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

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Knowing how consumers perceive travel websites can help practitioners better understand consumers’ online requirements. This paper reports the findings of a longitudinal study that investigated the changes and trends in the profile and behavior of online travel-website users in Hong Kong. The profiles of e-buyers and e-browsers in 2009, when compared with those established by prior studies conducted in 2000 and 2007, point in a new direction for practitioners and researchers investigating online travelwebsite user behavior. The results indicated that more middle-aged consumers have become online travel-website users, and that website security and price are perceived to be the most important factors for travel-website use by both e-browsers and e-buyers.

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The phenomenon of at-destination search activity and decision processes utilized by visitors to a location is predominantly an academic unknown. As destinations and organizations increasingly compete for their share of the travel dollar, it is evident that more research need to be done regarding how consumers obtain information once they arrive at a destination. This study examined visitor referral recommendations provided by hotel and non-hotel ''locals" in a moderately-sized community for lodging, food service, and recreational and entertainment venues.

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Travel Law, Cases and Materi als, by Robert M. Jarvis, John R. Goodwin, William D. Henslee (Durham, N.C.: Carolina Academic Press, 19981, ISBN 0-89089-802-2,1998, vii + 738 pp., including tables, acknowledgments, appendices, index. $80 hardback.