How does multi‐modal travel enhance tourist attraction accessibility? A refined two‐step floating catchment area method using multi‐source data

Author:

Zhang Yongqi123ORCID,Fu Xiao123ORCID,Yu Zhaoyuan45,Luo Shuli6

Affiliation:

1. School of Transportation Southeast University Nanjing China

2. Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport Nanjing China

3. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast University Nanjing China

4. Key Laboratory of Virtual Geographic Environment Ministry of Education Nanjing China

5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography Nanjing Normal University Nanjing China

6. School of Humanities and Social Science The Chinese University of Hong Kong Shenzhen China

Abstract

AbstractIn the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities.

Funder

National Natural Science Foundation of China

Humanities and Social Science Fund of Ministry of Education of China

Publisher

Wiley

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