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Traffic related activity pattern of Chinese adults: a nation-wide population based survey

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 482–489 (2023)Cite this article

Abstract

Background

Traffic-related air pollutants lead to increased risks of many diseases. Understanding travel patterns and influencing factors are important for mitigating traffic exposures. However, there is a lack of national large-scale research.

Objective

This study aimed to evaluate the daily travel patterns of Chinese adults and provide basic data for traffic exposure and health risk research.

Methods

We conducted the first nation-wide survey of travel patterns of adults (aged 18 and above) in China during 2011–2012. We conducted a cross-sectional study based on a nationally representative sample of 91, 121 adults from 31 provinces in China. We characterized typical travel patterns by cluster analysis and identified the associated factors of each pattern using multiple logistic regression and generalized linear regression models.

Results

We found 115 typical daily travel patterns of Chinese adults and the top 11 accounted for 94% of the population. The interaction of age, urban and rural areas, income levels, gender, educational levels, city population and temperature affect people’s choice of travel patterns. The average travel time of Chinese adults is 45 ± 40 min/day, with the longest travel time by the combination of walking and car (70 min/day). Gender has the largest effect on travel time (B = −8.94, 95% CI: −8.95, −8.93), followed by city GDP (B = −4.23, 95% CI: −4.23, −4.22), urban and rural areas (B = −3.62, 95% CI: −3.63, −3.61), age (B = −2.21, 95% CI: −2.21, −2.2), educational levels (B = −1.53, 95% CI: −1.53, −1.52), city area (B = −1.4, 95% CI: −1.4, −1.39) and temperature (B = 1.21, 95% CI: 1.2, 1.21).

Significance

This study was the first nation-wide study on traffic activity patterns in China, which provides basic data for traffic exposure and health risk research and provides the basis for the state to formulate transportation-related policies.

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Fig. 1: Percentage (black circle and dashed line, %) of Chinese adults in the top 11 travel patterns and their travel time (median ± interquartile range (IQR), min/d).
Fig. 2: Distribution of travel number and travel time for the top 11 travel patterns in China, left and the upper tree structure is for clustering.
Fig. 3: Comparison of regional population and GDP data for 2011 and 2019.

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Data availability

The data that support the findings of this study are available from the corresponding author, (XD), upon reasonable request.

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Acknowledgements

We thank all the study participants and field investigators.

Funding

This work was supported by the National Science Foundation [grant numbers 41977374].

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Authors and Affiliations

  1. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China

    Nan Jiang, Ling Qi, Beibei Wang, Suzhen Cao, Ning Qin & Xiaoli Duan

  2. Chinese Center for Disease Control and Prevention, Beijing, China

    Limin Wang

  3. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, China

    Bin Zou

  4. Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY, USA

    Kai Zhang

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Contributions

NJ: Methodology, writing—review and editing, writing—original draft, visualization. LQ: Formal analysis, writing—review and editing. BW: Methodology, investigation, data curation. SC: Writing—review and editing. LW: Methodology, investigation, data curation, writing—review and editing. BZ: Resources, data curation. KZ: Writing—review and editing. NQ: Writing—review and editing. XD: Conceptualization, methodology, investigation, data curation, writing—review and editing, supervision, project administration, funding acquisition.

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Correspondence to Xiaoli Duan.

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Jiang, N., Qi, L., Wang, B. et al. Traffic related activity pattern of Chinese adults: a nation-wide population based survey. J Expo Sci Environ Epidemiol 33, 482–489 (2023). https://doi.org/10.1038/s41370-022-00469-y

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