Advertisement

Modeling the Transport Mode Choice Behavior of Motorcyclists

  • Ali Shahikhaneh
  • Kian Ahmadi Azari
  • Iman AghayanEmail author
Research Paper
  • 2 Downloads

Abstract

In this study, the mode choice behavior model is used to determine the key parameters influencing the motorcyclists’ mode choice. The findings can be used to shift the motorcycle mode choice toward public transport to reduce fatalities, injuries and economic losses. In order to create the proposed model, the stated-preference method and nested logit modeling are used to investigate the attributes affecting the individuals’ mode choice among three types of transportation including motorcycle, bus and LRT. The results indicated that two main attributes, namely traveling time and cost were involved in affecting the behavior of motorcyclists’ mode choice. However, the traveling time was taken into consideration more for choosing motorcycle mode. Considering the travel time elasticity, as the travel time increased up to 1%, the demand for using motorcycle decreased about 0.6% while the demand for using bus and LRT increased up to 1.3 and 0.9%, respectively. Moreover, other attributes such as the increased age, an increased monthly income and private car ownership may encourage motorcyclists to switch their mode choice toward other types of transportation.

Keywords

Motorcycle Mode choice Nested logit modeling Stated-preference method 

References

  1. Azari KA, Arintono S, Hamid H, Rahmat R (2013a) Modelling demand under parking and cordon pricing policy. Transp Policy 25:1–9.  https://doi.org/10.1016/j.tranpol.2012.10.003 CrossRefGoogle Scholar
  2. Azari K, Arintono S, Hamid H, Davoodi S (2013b) Evaluation of demand for different trip purposes under various congestion pricing scenarios. J Transp Geogr 29:43–51.  https://doi.org/10.1016/j.jtrangeo.2013.01.001 CrossRefGoogle Scholar
  3. Basu D, Hunt J (2012) Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: a stated preference approach. Transp Res Part A Policy Pract 46(9):1465–1476.  https://doi.org/10.1016/j.tra.2012.05.010 CrossRefGoogle Scholar
  4. Ben-Akiva M, Lerman S (1991) Discrete choice analysis: theory and application to travel demand. The MIT Press, CambridgeGoogle Scholar
  5. Burg P, Fox J, Kouwenhoven M, Rohr C, Wigan M (2007) Modeling of motorcycle ownership and commuter usage. Transp Res Rec 1818:39–46Google Scholar
  6. Chakrabarti S (2016) How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles. Transp Policy 5:10.  https://doi.org/10.1016/j.tranpol.2016.11.005 Google Scholar
  7. Chang H, Wu S (2008) Exploring the vehicle dependence behind mode choice: evidence of motorcycle dependence in Taipei. Transp Res Part A Policy Pract 42(2):307–320.  https://doi.org/10.1016/j.tra.2007.10.005 CrossRefGoogle Scholar
  8. Chen C, Lai W (2011) The effects of rational and habitual factors on mode choice behaviors in a motorcycle-dependent region: evidence from Taiwan. Transp Policy 18(5):711–718.  https://doi.org/10.1016/j.tranpol.2011.01.006 CrossRefGoogle Scholar
  9. Dissanayake D, Morikawa T (2010) Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: a case study in Bangkok Metropolitan Region. J Transp Geogr 18(3):402–410.  https://doi.org/10.1016/j.jtrangeo.2009.07.003 CrossRefGoogle Scholar
  10. Fatima E, Kumar R (2014) Introduction of public bus transit in Indian cities. Int J Sustain Built Environ 3(1):27–34.  https://doi.org/10.1016/j.ijsbe.2014.06.001 CrossRefGoogle Scholar
  11. Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis: a primer. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  12. Ibrahim Sheikh A, Radin Umar R, Habshah M, Kassim H, Stevenson M, Hariza A (2006) Mode choice model for vulnerable motorcyclists in Malaysia. Traffic Inj Prev 7(2):150–154.  https://doi.org/10.1080/15389580600550354 CrossRefGoogle Scholar
  13. Jones S, Gurupackiam S, Walsh J (2013a) Factors influencing the severity of crashes caused by motorcyclists: analysis of data from alabama. J Transp Eng 139(9):949–956CrossRefGoogle Scholar
  14. Jones L, Cherry C, Vu T, Nguyen Q (2013b) The effect of incentives and technology on the adoption of electric motorcycles: a stated choice experiment in Vietnam. Transp Res Part A Policy Pract 57:1–11.  https://doi.org/10.1016/j.tra.2013.09.003 CrossRefGoogle Scholar
  15. Mashhad Transportation and Traffic Organization (MTTO) (2010) Sixth Mashhad transport report. Retrieved from http://www.mashadtraffic.ir/portal/images/stories/taffic/pdf/amarnameh/amarname%2089.pdf
  16. Mashhad Transportation and Traffic Organization (MTTO) (2011) Restricted traffic zone. Retrieved from http://www.mashadtraffic.ir/portal/1387-12-20-06-41-09.html
  17. Mashhad Transportation and Traffic Organization (MTTO) (2014) Mashhad. Retrieved from http://www.mashadtraffic.ir/portal/images/amarname-92.pdf
  18. Miskeen AB, Manssour A, Mohamed Alhodairi A, Rahmat RAABO (2013) Modeling of intercity transport mode choice behavior in Libya: a binary logit model for business trips by private car and intercity bus. Aust J Basic Appl Sci 7(1):302–311Google Scholar
  19. Nurdden A, Rahmat RAOK, Ismail A (2007) Effect of transportation policies on modal shift from private car to public transport in Malaysia. J Appl Sci 7(7):1013–1018CrossRefGoogle Scholar
  20. Sharif University of Technology Center for Transportation Research (SUTCRT) (1997) Travel demand estimation for future. Comprehensive transportation study of MashhadGoogle Scholar
  21. Tran N, Chikaraishi M, Zhang J, Fujiwara A (2012) Exploring day-to-day variations in the bus usage behavior of motorcycle owners in Hanoi. Procedia Soc Behav Sci 43:265–276.  https://doi.org/10.1016/j.sbspro.2012.04.099 CrossRefGoogle Scholar
  22. Tran M, Zhang J, Fujiwara A (2014) Can we reduce the access by motorcycles to mass transit systems in future Hanoi? Procedia Soc Behav Sci 138:623–631.  https://doi.org/10.1016/j.sbspro.2014.07.248 CrossRefGoogle Scholar
  23. Van H, Choocharukul K, Fujii S (2014) The effect of attitudes toward cars and public transportation on behavioral intention in commuting mode choice—a comparison across six Asian countries. Transp Res Part A Policy Pract 69:36–44.  https://doi.org/10.1016/j.tra.2014.08.008 CrossRefGoogle Scholar
  24. Vedagiri P, Arasan VT (2009) Modelling modal shift due to the enhanced level of bus service. Transport 24(2):121–128CrossRefGoogle Scholar
  25. Wang Y, Wang Z, Li Z, Staley S, Moore A, Gao Y (2013) Study of modal shifts to bus rapid transit in Chinese cities. J Transp Eng 139(5):515–523.  https://doi.org/10.1061/(asce)te.1943-5436.0000523 CrossRefGoogle Scholar
  26. Wigan M (2002) Motorcycles as a full mode of transportation. Transp Res Rec 1818:39–46CrossRefGoogle Scholar
  27. Yagi S, Nobel D, Kawaguchi H (2012) Time series comparison of auto and motorcycle ownership and mode choice in a changing transportation environment. Transp Res Rec 2317:40–50CrossRefGoogle Scholar
  28. Ye L, Wang Q (2011) Case study of motorcycle use and policy analysis in Huizhou, China. J Transp Eng 137(11):831–836CrossRefGoogle Scholar
  29. Zhang Z, Guan H, Qin H, Xue Y (2013) A traffic mode choice model for the bus user groups based on SP and RP data. Procedia Soc Behav Sci 96:382–389.  https://doi.org/10.1016/j.sbspro.2013.08.045 CrossRefGoogle Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, Science and Research BranchIslamic Azad UniversityShahroodIran
  2. 2.BETA Consulting CompanyMashhadIran
  3. 3.Department of Civil EngineeringShahrood University of TechnologyShahroodIran

Personalised recommendations