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Identification of contributing factors on travel mode choice among different resident types with bike-sharing as an alternative

Identification of contributing factors on travel mode choice among different resident types with bike-sharing as an alternative

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This study mainly studies the contributing factors on residents’ travel mode choices after the emergence of bike-sharing. In contrast to existing studies, the authors divided the travellers into commuters, students, and other travellers by travel purposes, and analysed their travel mode choice by a mix logit model, respectively. It is found that the factors on residents’ travel mode choices have many similarities and differences. Gender, private car ownership, travel cost, travel distance, and travel time are the common factors for all travellers; economy and comfort preference are the factors that affect commuters and students; commuters and other travellers are affected by age, income, and safety preference. However, occupation and an environmental preference are unique significant factors on commuters; students are affected by owning a bike; and a good understanding of bike-sharing is the only significant factor that affects other travellers. In addition, comfort preference has a significant negative influence on the choice of public transport and bike-sharing for students, while it has a significant positive impact on the choice of a private car for commuters.

References

    1. 1)
      • 25. Guo, Y.Y., Li, Z.B., Wu, Y., et al: ‘Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach’, Transp. Res. F, Traffic Psychol. Behav., 2018, 59, pp. 212221.
    2. 2)
      • 9. Zhang, R., Yao, E., Liu, Z.: ‘School travel mode in Beijing, China’, J. Transp. Geogr., 2017, 62, pp. 98110.
    3. 3)
      • 12. Spencer, P., Watts, R., Vivanco, L., et al: ‘The effect of environmental factors on bicycle commuters in Vermont: influences of a northern climate’, J. Transp. Geogr., 2013, 31, pp. 1117.
    4. 4)
      • 15. Rubin, O., Mulder, C.H., Bertolini, L.: ‘The determinants of mode choice for family visits-evidence from Dutch panel data’, J. Transp. Geogr., 2014, 38, pp. 137147.
    5. 5)
      • 5. Li, W., Kamargianni, M.: ‘Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: a mode choice mode and policy simulation for Taiyuan-China’, Transp. Res. A, Policy Pract., 2018, 111, pp. 277291.
    6. 6)
      • 7. Haustein, S., Thorhauge, M., Cherchi, E.: ‘Commuter’ attitudes and norms related to travel time and punctuality: a psychographic segmentation to reduce congestion’, Travel Behav. Soc., 2018, 12, pp. 4150.
    7. 7)
      • 8. Ye, R., Helena, T.: ‘Satisfaction with the commute: the role of travel mode choice, built environment and attitudes’, Transp. Res. D, Transp. Environ., 2017, 52, pp. 535547.
    8. 8)
      • 22. Moore, D.N., Schneider, W.H.IV, Savolainen, P.T., et al: ‘Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations’, Accident Anal. Prev., 2011, 43, pp. 621630.
    9. 9)
      • 19. Pal, A., Zhang, Y.: ‘Free-floating bike sharing: solving real-life large-scale staticrebalancing problems’, Transp. Res. C, Emerg. Technol., 2017, 80, pp. 92116.
    10. 10)
      • 26. Guo, Y.Y., Osama, A., Sayed, T.: ‘A cross-comparison of different techniques for modeling macro-level cyclist crashes’, Accident Anal. Prev., 2018, 113, pp. 3846.
    11. 11)
      • 10. Zhan, G., Yan, X., Zhu, S., et al: ‘Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China’, Transp. Policy, 2016, 45, pp. 5565.
    12. 12)
      • 20. Faghih-Imani, A., Anowar, S., Miller, E.J., et al: ‘Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York city’, Transp. Res. A, Policy Pract., 2017, 101, pp. 1121.
    13. 13)
      • 18. Darren, M.S., Celenna, C.: ‘What factors influence bike share ridership? An investigation of Hamilton, Ontario's bike share hubs’, Travel Behav. Soc., 2019, 16, pp. 5058.
    14. 14)
      • 6. Jia, N., Li, L., Ling, S., et al: ‘Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – a cross-city study in China’, Transp. Res. A, Policy Pract., 2018, 111, pp. 108118.
    15. 15)
      • 27. Guo, Y.Y., Li, Z.B., Liu, P., et al: ‘Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model’, Accident Anal. Prev., 2019, 128, pp. 164174.
    16. 16)
      • 14. He, S.Y., Thøgersen, J.: ‘The impact of attitudes and perceptions on travel mode choice and car ownership in a Chinese megacity: the case of Guangzhou’, Res. Transp. Econ., 2017, 62, pp. 5767.
    17. 17)
      • 24. Guo, Y.Y., Li, Z.B., Wu, Y., et al: ‘Exploring unobserved heterogeneity in bicyclists’ red-light running behaviors at different crossing facilities’, Accident Anal. Prev., 2018, 115, pp. 118127.
    18. 18)
      • 21. Campbell, K.B., Brakewood, C.: ‘Sharing riders: how bike-sharing impacts bus ridership in New York city’, Transp. Res. A, Policy Pract., 2017, 100, pp. 264282.
    19. 19)
      • 17. Geng, J., Long, R., Chen, H.: ‘Impact of information intervention on travel mode choice of urban residents with different goal frames: a controlled trial in Xuzhou, China’, Transp. Res. A, Policy Pract., 2016, 91, pp. 134147.
    20. 20)
      • 4. Tsinghua Tongheng Urban Planning & Design Institute (THUPDI): ‘Bike-sharing and the City 2017 White Paper’. Available at http//www.sohu.com/a/133766880_58110, accessed 2017, in Chinese.
    21. 21)
      • 23. Gong, L., Fan, W.: ‘Modeling single-vehicle run-off-road crash severity in rural areas: accounting for unobserved heterogeneity and age difference’, Accident Anal. Prev., 2017, 101, pp. 124134.
    22. 22)
      • 2. Faghih-Imani, A., Hampshire, R., Marla, L., et al: ‘An empirical analysis of bike sharing usage and rebalancing: evidence from Barcelona and Seville’, Transp. Res. A, Policy Pract., 2017, 97, pp. 177191.
    23. 23)
      • 11. Habib, K.N., Mann, J., Mahmoud, M., et al: ‘Synopsis of bicycle demand in the city of Toronto: investigating the effects of perception, consciousness and comfortability on the purpose of biking and bike ownership’, Transp. Res. A, Policy Pract., 2014, 70, pp. 6780.
    24. 24)
      • 13. Hu, H., Xu, J., Shen, Q., et al: ‘Travel mode choices in small cities of China: a case study of Changting’, Transp. Res. D, Transp. Environ., 2018, 59, pp. 361374.
    25. 25)
      • 3. Campbell, A.A., Cherry, C.R., Ryerson, M.S., et al: ‘Factors influencing the choice of shared and shared electric bikes in Beijing’, Transp. Res. C, Emerg. Technol., 2016, 67, pp. 399414.
    26. 26)
      • 1. Bachand-Marleau, J., Lee, B.H.Y., El-Geneidy, A.M.: ‘Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use’, Transp. Res. Rec., J. Transp. Res. Board, 2012, 2314, pp. 6671.
    27. 27)
      • 16. Habib, K.N., Weiss, A.: ‘Evolution of latent modal captivity and mode patterns for commuting trips: a longitudinal analysis using repeated cross-sectional data sets’, Transp. Res. A, Policy Pract., 2014, 66, pp. 3951.
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