Research Output
What Encourages People to Carpool? An Evaluation of Factors with Meta-analysis
  Non-household carpools (where two or more commuters from different residences travel together in the same private vehicle) bring public benefits. To encourage and incentivise it, transport practitioners and researchers must understand its private motivations and deterrents. Existing studies often report conflicting results or non-generalisable findings. Thus, a quantitative systematic review of the literature body is needed. Using meta-analysis, this study synthesised 22 existing empirical studies (representing over 79,000 observations) to produce an integrated review of the carpooling literature. The meta-analysis determined 24 non-household carpooling factors, and their effect sizes. Factors such as number of employees (r¯=0.42), partner matching programs (r¯=0.42), female (r¯=0.22) and fixed work schedule (r¯=0.15) were found to have strong effects on carpooling while judgmental factors (such as the motivation to save costs) only exhibited small influence (r¯<0.1). Based on the significant effects, the paper discussed prospects for improving carpooling uptake by developing: (i) target demographics, (ii) selling points for marketing, (iii) carpooling partner programs and (iv) multiple employer ‘super-pools’. The results warrant caution due to the small amount of studies synthesised. Transport practitioners might plan carpooling policies based on the findings; and transportation researchers might use the list of factors to model carpooling behaviour.

  • Type:

    Article

  • Date:

    16 September 2015

  • Publication Status:

    Published

  • DOI:

    10.1007/s11116-015-9661-7

  • ISSN:

    0049-4488

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Neoh, J., Chipulu, M., & Marshall, A. (2017). What Encourages People to Carpool? An Evaluation of Factors with Meta-analysis. Transportation, 44, 423–447. https://doi.org/10.1007/s11116-015-9661-7

Authors

Keywords

Carpool; Liftshare; Rideshare; Meta-analysis; Transport demand management

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