Optimization of tourism routes in Lushunkou District based on ArcGIS

PLoS One. 2022 Mar 11;17(3):e0264526. doi: 10.1371/journal.pone.0264526. eCollection 2022.

Abstract

With the advancements and developments in China's tourism industry, various autonomous forms of tourism have been gaining prominence. As such, to facilitate tourists and provide them with maximum experience while economizing on time and cost is essential. One approach toward achieving this is the optimization of tourism routes. However, so far the studies on this approach have focused primarily on inland tourist sites and have lacked a geographic perspective. Therefore, this study undertook the tourism resource data of Lushunkou District of 2020, used the ArcGIS accessibility evaluation model to analyze tourism resources, and finally used the Vehicle Routing Problem of network analysis technology to optimize the tourism route of Lushunkou District and obtain the general overall intellectual framework and technical methods for tourism route optimization. The results showed that the ArcGIS accessibility evaluation model could be used to integrate resources in the tourism area before using the Vehicle Routing Problem to optimize the analysis of tourism routes, thereby enabling the separation of different types of tourism. These divisions were based on the Vehicle Routing Problem to optimize routes for one-day and two-day tours. A new method and model for optimization for tourism routes was constructed to provide a basis and reference for the optimization of tourism routes in similar cities. The observations and results of the present study can facilitate the government in developing the tourism industry and maximizing the benefits obtained from them. Further, travel agencies and tourists will have the provision of designing optimum tourism routes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cities
  • Industry
  • Tourism*
  • Travel*

Grants and funding

This research study was supported by the National Natural Science Foundation of China (grant no. 41701123). Qian Pei wrote the main manuscript text, conducted the experiment and analyzed the data; Li Wang contributed to all aspects of this work; Peng Du provided funding acquisition and Zhaolan Wang revised the paper. All authors reviewed the manuscript.