Modern tourism in the 21st century is facing numerous challenges. One of these challenges is the rapidly growing number of tourists in space limited regions such as historical city centers, museums or geographical bottlenecks like narrow valleys. In this context, a proper and accurate prediction of tourism volume and tourism flow within a certain area is important and critical for visitor management tasks such as visitor flow control and prevention of overcrowding. Static flow control methods like limiting access to hotspots or using conventional low level controllers could not solve the problem yet. In this paper, we empirically evaluate the performance of several state-of-the-art deep-learning methods in the field of visitor flow prediction with limited data by using available granular data supplied by a tourism region and comparing the results to ARIMA, a classical statistical method. Our results show that deep-learning models yield better predictions compared to the ARIMA method, while both featuring faster inference times and being able to incorporate additional input features.
翻译:21世纪现代旅游业正面临众多挑战,其中一项挑战是空间有限地区游客人数迅速增加,如历史城市中心、博物馆或狭窄山谷等地理瓶颈;在这方面,适当和准确地预测某一区域内的旅游量和旅游流量,对于游客管理任务,如控制游客流量和防止过度拥挤等至关重要;限制进入热点或使用传统低层控制器等静态流动控制方法,尚无法解决问题;在本文件中,我们利用旅游区域提供的现有颗粒数据,将结果与典型的统计方法ARIMA(ARIMA)进行比较,以有限的数据对游客流动预测领域若干最先进的深学习方法的绩效进行了实证评估;我们的结果表明,深层次学习模式与ARIMA方法相比产生更好的预测,同时具有更快的推论时间和能够纳入更多输入特征。