Thematic cultural heritage tourism trail planning integrating multi-source data and machine learning in Wuhan China

Han Zou; Guoliang Zhang; Cong Sun; Lisa Landrum; Yuchen Tang; Yu Hu; Wen Cheng; Aoqiang Li

doi:10.1038/s40494-025-02081-3

Thematic cultural heritage tourism trail planning integrating multi-source data and machine learning in Wuhan China

Han Zou
, Guoliang Zhang
, Cong Sun^*
, Lisa Landrum
, Yuchen Tang
, Yu Hu
, Wen Cheng
, Aoqiang Li

^*Corresponding author for this work

Department of Architecture and Civil Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

4 Downloads (CityUHK Scholars)

Abstract

Nowadays, Cultural heritage tourism faces challenges in route planning, including weak data-mining capacity, limited multi-indicator evaluation, and inefficiencies in traditional pathfinding. This study proposes an innovative thematic and sustainable framework that integrates advanced digital technologies at both meso- and micro-spatial scales to optimize heritage route planning. The study introduces and applies the Non-dominated Sorting Genetic Algorithm III (NSGA-III)—specifically designed for high-dimensional multi-objective optimization—which outperforms existing methods in key aspects and effectively solves complex route optimization problems under multiple constraints. Experimental results confirm that Nsga3ip demonstrating 97% rational route probability and 0.89 optimization efficiency—surpassing MOPSO (83%, 0.62) and random algorithms (12%, 0.19) under identical constraints. The findings demonstrate its strengths in planning quality, enhancement of heritage value, and practicability. This underscores the method’s innovation and applicability, further promoting the integration of data-driven approaches in heritage conservation and interdisciplinary urban research. © The Author(s) 2025.

Original language	English
Article number	547
Journal	npj Heritage Science
Volume	13
Issue number	1
Online published	29 Oct 2025
DOIs	https://doi.org/10.1038/s40494-025-02081-3
Publication status	Published - 2025

Funding

We would like to thank Professor Baihao Li and Professor Mingxing Hu from Southeast University for their guidance on the paper. This research was funded by National Natural Science Foundation of China (grant number: 52378052), China Scholarship Council(grant number: 202308420087), 2024 Guangdong Philosophy and Social Science Foundation Regular Project(grant number: GD24CYS15), Shenzhen Research Initiation Funding for High-Level, Precision, and Critically-Needed Talents (grant number: 827-000827), Hubei University of Technology Green Industry Science and Technology Leading Program (grant number: XJ2021005501), Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes(grant number: 2020EJB004).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production

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title = "Thematic cultural heritage tourism trail planning integrating multi-source data and machine learning in Wuhan China",

abstract = "Nowadays, Cultural heritage tourism faces challenges in route planning, including weak data-mining capacity, limited multi-indicator evaluation, and inefficiencies in traditional pathfinding. This study proposes an innovative thematic and sustainable framework that integrates advanced digital technologies at both meso- and micro-spatial scales to optimize heritage route planning. The study introduces and applies the Non-dominated Sorting Genetic Algorithm III (NSGA-III)—specifically designed for high-dimensional multi-objective optimization—which outperforms existing methods in key aspects and effectively solves complex route optimization problems under multiple constraints. Experimental results confirm that Nsga3ip demonstrating 97\% rational route probability and 0.89 optimization efficiency—surpassing MOPSO (83\%, 0.62) and random algorithms (12\%, 0.19) under identical constraints. The findings demonstrate its strengths in planning quality, enhancement of heritage value, and practicability. This underscores the method{\textquoteright}s innovation and applicability, further promoting the integration of data-driven approaches in heritage conservation and interdisciplinary urban research. {\textcopyright} The Author(s) 2025.",

author = "Han Zou and Guoliang Zhang and Cong Sun and Lisa Landrum and Yuchen Tang and Yu Hu and Wen Cheng and Aoqiang Li",

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AU - Zou, Han

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AU - Sun, Cong

AU - Landrum, Lisa

AU - Tang, Yuchen

AU - Hu, Yu

AU - Cheng, Wen

AU - Li, Aoqiang

PY - 2025

Y1 - 2025

N2 - Nowadays, Cultural heritage tourism faces challenges in route planning, including weak data-mining capacity, limited multi-indicator evaluation, and inefficiencies in traditional pathfinding. This study proposes an innovative thematic and sustainable framework that integrates advanced digital technologies at both meso- and micro-spatial scales to optimize heritage route planning. The study introduces and applies the Non-dominated Sorting Genetic Algorithm III (NSGA-III)—specifically designed for high-dimensional multi-objective optimization—which outperforms existing methods in key aspects and effectively solves complex route optimization problems under multiple constraints. Experimental results confirm that Nsga3ip demonstrating 97% rational route probability and 0.89 optimization efficiency—surpassing MOPSO (83%, 0.62) and random algorithms (12%, 0.19) under identical constraints. The findings demonstrate its strengths in planning quality, enhancement of heritage value, and practicability. This underscores the method’s innovation and applicability, further promoting the integration of data-driven approaches in heritage conservation and interdisciplinary urban research. © The Author(s) 2025.

AB - Nowadays, Cultural heritage tourism faces challenges in route planning, including weak data-mining capacity, limited multi-indicator evaluation, and inefficiencies in traditional pathfinding. This study proposes an innovative thematic and sustainable framework that integrates advanced digital technologies at both meso- and micro-spatial scales to optimize heritage route planning. The study introduces and applies the Non-dominated Sorting Genetic Algorithm III (NSGA-III)—specifically designed for high-dimensional multi-objective optimization—which outperforms existing methods in key aspects and effectively solves complex route optimization problems under multiple constraints. Experimental results confirm that Nsga3ip demonstrating 97% rational route probability and 0.89 optimization efficiency—surpassing MOPSO (83%, 0.62) and random algorithms (12%, 0.19) under identical constraints. The findings demonstrate its strengths in planning quality, enhancement of heritage value, and practicability. This underscores the method’s innovation and applicability, further promoting the integration of data-driven approaches in heritage conservation and interdisciplinary urban research. © The Author(s) 2025.

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Thematic cultural heritage tourism trail planning integrating multi-source data and machine learning in Wuhan China

Abstract

Funding

UN SDGs

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