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Investigating inter-generational changes in activity-travel behavior: a disaggregate approach

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Abstract

Investigation into the long-term trends in travel behavior is crucial for strategic development of transport systems and decisions about investment in transport infrastructures. There has been a debate about if there is a constant amount of time allocated for travel and thus an upper limit to daily travel demand. Recent studies have suggested a stagnated and even declining trend of car ownership and travel demand in developed countries (the so-called ‘peak car’ and ‘peak travel’ phenomenon). This study aims at exploring the possible long-term trends in activity-travel behavior in Hong Kong to shed some light on the ‘peak travel’ debate. We have acquired two large-scale datasets from Hong Kong’s Travel Characteristics Survey conducted a decade apart and applied propensity score matching to match individuals of similar socioeconomic backgrounds from different time periods and compare their activity-travel behavior. Results indicate that households and individuals with similar socioeconomic backgrounds at the two periods have distinctively different car ownership levels and daily travel and activity behaviors.

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Acknowledgements

Funding was provided by Research Grants Council, University Grants Committee of Hong Kong (Grant No. HKBU12656716). A short version of the paper has been presented at the World Symposium on Transport and Land Use Research, July 3rd- July 6th, 2017, Brisbane, Australia.

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Authors and Affiliations

  1. Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong

    Meng Zhou & Donggen Wang

  2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China

    Meng Zhou

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  1. Meng Zhou
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  2. Donggen Wang
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Correspondence to Donggen Wang.

Appendix

Appendix

This appendix mainly provides detailed outcomes of the analysis that do not fit into scope of the main text. Among these tables, Tables 11, 12, 13 and 14 are the logistic regression modeling results for propensity score estimation with sample ascription as the dependent variable (year of 2002 as 1 and year of 2011 as 0). Tables 15, 16, 17 and 18 show the balancing scores in all subsample analyses and Table 19 demonstrates the full results of inter-generational changes in activity-travel behavior.

Table 11 Propensity score estimation models for household car ownership analysis
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Table 12 Propensity score estimation models for license possession analysis
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Table 13 Propensity score estimation models for individual trip frequency analysis
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Table 14 Propensity score estimation models for time allocation analysis
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Table 15 Balancing scores of household car ownership models
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Table 16 Balancing scores of driving license possession models
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Table 17 Balancing scores of individual trip frequency analysis
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Table 18 Balancing scores of individual time allocation analysis
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Table 19 Full results of inter− generational changes in activity− travel behavior
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Zhou, M., Wang, D. Investigating inter-generational changes in activity-travel behavior: a disaggregate approach. Transportation 46, 1643–1687 (2019). https://doi.org/10.1007/s11116-018-9863-x

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