时空协同的城市旅游行程规划模型构建

传统的旅游行程规划往往仅被归类为空间排序问题,而对行程安排中时空耦合性与游客自身选择的灵活性考虑不足。本文将行程规划问题的理解从空间视角拓展至游客活动视角,从旅游节点时空耦合关系以及重构模式出发,将旅行中涉及到的时间、空间与专题等多维属性有机组织,进而提出行程时空链的概念模型以及行程要素时空衔接方法。研究以南京为案例展开模型实验,结果显示该模型与传统线路设计方法在节点名称、节点数量以及节点顺序等指标的匹配度均超过了80%,体现出较好的应用可靠性。相较于已有研究仅从空间视角展开的论述,本文提出以游客自身作为行程规划的基本单位,从游客行为选择、游览时间序列以及节点线路串联等多维视角进行映射耦合,以期为拓展深化城市旅游线路设计理论,支撑智慧旅游工程精细化建设提供理论支撑与借鉴。

Urban Tourism Itinerary Planning from a Spatiotemporal Perspective

Traditionally, tourism itinerary planning is implemented as a spatial arrangement issue, which lacks the consideration of the spatiotemporal coupling and the flexibility for tourists to make choices. In this paper, the understanding of the itinerary planning problem was extended from the perspective of space to the perspective of tourist activities. From the time-space coupling relationship and reconstruction mode of tourism nodes, the multi-dimensional attributes such as time, space, and topic involved in the travel were organically organized, and then the travel's spatiotemporal chain was proposed. The conceptual model and the method of space-time convergence of the stroke elements. The proposed method was applied to the case study of Nanjing, Jiangsu Province of China. Results show that the match between the model and the traditional itinerary design method in terms of node name, number of nodes, and node order exceeds 80%, indicating good methodological reliability. Compared with existing itinerary planning studies, this research took the basic information of the itinerary (such as travel time, cost budget, departure place, destination, etc.) as the precondition, and considered the travel itinerary from the perspective of tourists. Specifically, the spatiotemporal characteristics of the tourist nodes were organically integrated. While satisfying the spatial order of tourist routes, the rationality of the time arrangement of each tourism element was also considered. The proposed algorithm is mainly used to serve independent tourists. Meanwhile, this algorithm has the advantage of arranging route and schedule flexibly. It should be noted that the specific application of the model is still constrained by the basic travel itinerary conditions. The flexibility of the proposed model for meeting tourists' individualized needs is currently not strong. Considering that tourists' demands are in reality often changing during traveling, it is necessary to further optimize the adaptability, flexibility, and stability of the proposed model.