ANNUAL CONGRESS 2025

Exploration of the Relationship between Travel Activity Chains and Self-Assessed Health of Rural Community Residents: Based on Multi-Source Data and Health Survey Questionnaire

Not scheduled

20m

Yildiz Technical University, Istanbul

Poster Track 03 | MOBILITY

Speaker

Mr Kehao Li (Wuhan University)

Description

At present, most of the population still live in rural China, and the allocation of health facilities in rural areas is generally weaker than that in urban areas. The influencing factors of healthy life of rural residents still need attention and research. This study investigates the relationship between the daily travel activity chains of rural community residents and their self-assessed health status, aiming to provide a scientific basis for optimizing the construction of rural health communities and their surrounding supporting facilities. Given the difficulty of data acquisition in rural areas, such research is rare in China. This study focuses on four new rural communities in Pujiang County, Chengdu City, Sichuan Province, with 178 residents randomly selected as samples. The study comprehensively uses multi-source data, including GPS data obtained from wearable positioning devices, POI data from AMap, and building vector data from MapWorld. The AT-DBSCAN algorithm is used to cluster travel stop points (Yang etal., 2018), and the time screening method is used to determine residents' place of residence information. The TF-IDF algorithm is used to infer grid functional zone attributes based on POI data, and then to deduce the type of stop activities (Chen etal., 2021). Meanwhile, building vector data is used to judge the travel activity characteristics in rural areas without POI data, and finally to construct the daily travel activity chains of residents. Further, the Word2Vec word embedding model is used to vectorize the activity chain data (Li etal., 2024), and K-means clustering analysis is used to obtain different daily travel activity pattern clusters. Combined with the self-rated health data of residents obtained from the SRHMS self-test health scale (Xu etal., 2003), ANOVA test is used to analyze the significant differences between different travel activity patterns and health scores. The results show that there are significant differences in self-rated health levels among residents with different travel activity patterns, which provides an important reference for the planning and health management of rural communities.

References

Yang, Z., Chao, Y., 2018. Anchors Identification in Trajectory Based on Temporospatial Clustering Algorithm. Journal of Transportation Systems Engineering and Information Technology 18, 88.
Chen, S., Zhang, H., Yang, H., 2021. Urban Functional Zone Recognition Integrating Multisource Geographic Data. Remote Sensing 13, 4732. https://doi.org/10.3390/rs13234732
Li, W., Zhang, Y., Chen, Y., Ding, L., Zhu, Y., Chen, X. (Michael), 2024. Multi-day activity pattern recognition based on semantic embeddings of activity chains. Travel Behaviour and Society 34, 100682. https://doi.org/10.1016/j.tbs.2023.100682
Xu, J., Guo, R., Liu, Y. S., Wang, P., Huang, W. Y., & Chen, Z. L., 2003. The study of responsiveness on self-rated health measurement scale (the revised version 1.0). Chinese Journal of Health Statistics, 20(05), 272-5.