This study uses meta-analysis to examine the relationship between estimated international tourism demand elasticities and the data characteristics and study features which may affect such empirical estimates. By reviewing 195 studies published during the period 1961-2011, the meta-regression analysis shows that origin, destination, time period, modeling method, data frequency, the inclusion/omission of other explanatory variables and their measures, and sample size all significantly influence the estimates of the demand elasticities generated by a model. Moreover, the demand elasticities at both product and destination levels are generalized by statistically integrating previous empirical estimates. The findings of this meta-analysis will be useful wherever an understanding of the drivers of tourism demand is critically important.
:Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980-2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modelling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.
We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to tackle the job shop scheduling problem (JSP).This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure and a reference solution determination method. To test the performance of TS/PR, we apply it to tackle almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20 years.
SUMMARYTraffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short-term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included.
This is the accepted version of the paper.This version of the publication may differ from the final published version.Permanent repository link: https://openaccess.city.ac.uk/id/eprint/21358/ Link to published version: http://dx.
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