RESUMEN

Forecasting is of utmost importance for the Tourism Industry. The development of models to predict visitation demand to specific places is essential to formulate adequate tourism development plans and policies. Yet, only a handful of models deal with the hard problem of fine-grained (per attraction) tourism demand prediction. In this paper, we argue that three key requirements of this type of application should be fulfilled: (i) recency-forecasting models should consider the impact of recent events (e.g. weather change, epidemics and pandemics); (ii) seasonality-tourism behavior is inherently seasonal; and (iii) model specialization-individual attractions may have very specific idiosyncratic patterns of visitations that should be taken into account. These three key requirements should be considered explicitly and in conjunction to advance the state-of-the-art in tourism prediction models. In our experiments, considering a rich set of indoor and outdoor attractions with environmental and social data, the explicit incorporation of such requirements as features into the models improved the rate of highly accurate predictions by more than 320% when compared to the current state-of-the-art in the field. Moreover, they also help to solve very difficult prediction cases, previously poorly solved by the current models. We also investigate the performance of the models in the (simulated) scenarios in which it is impossible to fulfill all three requirements-for instance, when there is not enough historical data for an attraction to capture seasonality. All in all, the main contributions of this paper are the proposal and evaluation of a new information architecture for fine-grained tourism demand prediction models as well as a quantification of the impact of each of the three aforementioned factors on the accuracy of the learned models. Our results have both theoretical and practical implications towards solving important touristic business demands.

Asunto(s)

Políticas , Turismo

RESUMEN

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our results also show that if the government had not taken any action and the only safety measure taken was the population's voluntary isolation (out of fear), the time until the contagion peak for the first wave would have been postponed, but its value would more than double.

Asunto(s)

COVID-19/transmisión , Movimiento , Brasil/epidemiología , COVID-19/epidemiología , COVID-19/patología , COVID-19/virología , Bases de Datos Factuales , Humanos , Modelos Teóricos , Pandemias , Política Pública , Cuarentena , SARS-CoV-2/aislamiento & purificación

RESUMEN

This paper aims to introduce our publicly available datasets in the area of tourism demand prediction for future experiments and comparisons. Most of the previous works in the area of tourism demand forecasting are based on coarse-grained analysis (level of countries or regions) and there are very few works and consequently datasets available for fine-grained tourism analysis (level of attractions and points of interest). In this article, we present our fine-grained enriched datasets for two types of attractions - (I) indoor attractions (27 Museums and Galleries in U.K.) and (II) outdoor attractions (76 U.S. National Parks) enriched with official number of visits, social media reviews and environmental data for each of them. In addition, the complete analysis of prediction results, methodology and exploited models, features' performance analysis, anomalies, etc, are available in our original paper, "Fine-grained tourism prediction: Impact of social and environmental features"[2].