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A clustering-based approach to quantifying socio-demographic impacts on urban mobility patterns

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  • Yang Yang
  • Samitha Samaranayake
  • Timur Dogan

Abstract

This paper uses a generalizable clustering approach to investigate the effects of socio-demographic features on aggregate urban mobility patterns, including activity distribution and travel modal split. We use K-means via principal component analysis to identify eight representative traveler clusters from the 2017 U.S. National Household Travel Survey. Based on the cluster centroids and the cluster percentages within a neighborhood, we can estimate a Temporal Mobility Choice Matrix ( TM ) that describes the neighborhood-level aggregate mobility choice pattern. The estimation accuracy is assessed in a case study in LA City. It is found that the neighborhood-level temporal mobility patterns are well-replicated, with an average R 2 of 65.47%, 53.15%, and 72.04% among all analyzed neighborhoods in the city. However, we find a moderate to low accuracy in estimating the spatial differences in the mobility patterns across neighborhoods. This could be because factors other than socio-demographics, such as physical and built environment factors like terrain, street quality, or amenity densities, are contributing to the spatial differences but have not been considered in this study. Overall, we show that socio-demographic features alone can approximate the average temporal mobility choice patterns of a given population. Our method and result can serve as the baseline and benchmark for future mobility studies that take the socio-demographics of the traveler population into consideration in modeling.

Suggested Citation

  • Yang Yang & Samitha Samaranayake & Timur Dogan, 2023. "A clustering-based approach to quantifying socio-demographic impacts on urban mobility patterns," Environment and Planning B, , vol. 50(9), pages 2452-2469, November.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:9:p:2452-2469
    DOI: 10.1177/23998083231159909
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    References listed on IDEAS

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    1. Maciej Kruszyna & Przemysław Śleszyński & Jeremi Rychlewski, 2021. "Dependencies between Demographic Urbanization and the Agglomeration Road Traffic Volumes: Evidence from Poland," Land, MDPI, vol. 10(1), pages 1-22, January.
    2. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    3. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    4. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2016. "Variability in daily activity-travel patterns: the case of a one-week travel diary," Post-Print halshs-01389479, HAL.
    5. Yang Xu & Shih-Lung Shaw & Ziliang Zhao & Ling Yin & Zhixiang Fang & Qingquan Li, 2015. "Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach," Transportation, Springer, vol. 42(4), pages 625-646, July.
    6. Miriam Magdolen & Sascha von Behren & Lukas Burger & Bastian Chlond, 2021. "Mobility Styles and Car Ownership—Potentials for a Sustainable Urban Transport," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
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