Binomial Logistic Regression to Analyze the Factors that Influence People’s Willingness to Cycle in the City of Banjarbaru, Indonesia

International Journal of Civil Engineering

© 2022 by SSRG - IJCE Journal

Volume 9 Issue 1

Year of Publication : 2022

Authors : Puguh Budi Prakoso, Iphan Fitrian Radam

10.14445/23488352/IJCE-V9I1P104

How to Cite?

Puguh Budi Prakoso, Iphan Fitrian Radam, "Binomial Logistic Regression to Analyze the Factors that Influence People’s Willingness to Cycle in the City of Banjarbaru, Indonesia," SSRG International Journal of Civil Engineering, vol. 9,  no. 1, pp. 25-31, 2022. Crossref, https://doi.org/10.14445/23488352/IJCE-V9I1P104

Abstract:

The increase of public interest in cycling in Banjarbaru is a good moment to promote non-motorized transport. However, cycling is frequently done as a hobby. Meanwhile, people's enthusiasm for cycling for daily transportation is very lacking. The study was conducted to analyze the influence factors of the people’s desire to cycle. Binomial logistic regression is used in analysis based on 154 questionnaires. Eighteen variables influenced cycling decisions from data processing results using SPSS. The most significant factors are human factors: cycle during the day time, travel time saving and bicycle path connection from residence to public places; facilities factors: availability of bicycle signs, bicycle lanes and CCTVs; environment factors: the traffic density and the existence of trees/green areas; safety and comfort factor: safer condition from crime; accessibility factors: the convenience of bringing a bicycle to public transport and short bike routes to public places; and distance factor: the distance between 2 and 2.5 km is favourable for cycling. It is figured out that the probability of people's willingness to cycle is currently only around 21%. However, when all the most important factors are fulfilled, the probability of people cycling can be raised to 70%.

Keywords:

Cycling, Influence factor of cycling, Willingness, Binomial logistic regression.

References:

[1] Lily Song, “Improving Access to Cycling Can Empower Women in Poor Areas,” 2021. [online] Available Https://Theconversation.Com/Memperbaiki-Akses-Bersepeda-Bisa-Memberdayakan-Perempuan-Di-Daerah-Miskin-134142 (2020) Accessed: November 22.
[2] Federal Highway Administration (FHWA), Reasons Why Bicycling and Walking are and are Not Being Used More Extensively as Travel Modes, FHWA National Bicycling & Walking Study, Usa, 1992.
[3] Meghan Winters, et al., “Motivators and Deterrents of Bicycling: Comparing Influences on Decisions to Ride,” Transportation, vol. 38, pp. 153-168, 2010. Crossref, http://dx.doi.org/10.1007/s11116-010-9284-y
[4] I. T. Mawening, B. Haryadi, B. H. Setiadji, “Facility Development Strategy to Increase Cycling Motivation of Community in Surakarta City,” Master's Thesis, Masters in Civil Engineering. Diponegoro University, 2014.
[5] Jachrizal Sumabrata, Tri Tjahjono, and Maulana Gituri, “Analysis Student Perception on Infrastructure and Willingness to Cycle at Universitas Indonesia Campus,” DEPOK. International Journal of Technology, vol. 6, 2015. Crossref, http://dx.doi.org/10.14716/ijtech.v6i2.987
[6] A. Hanavie, R. Setiawan, “Factors Affecting Students Using Bicycles,“ Journal of Civil Engineering Primary Dimensions, Petra Christian University Surabaya, 2014. 
[7] I. Indrawan, M. Z. Irawan, S. Malkhamah, “Factors for Students Reluctance to Cycling in Yogyakarta,“ Journal of Infrastructure & Facilities Asset Management, vol. 4, no. 4, 2020.
[8] I P. Windarni, A. Wulandari, F. R. Hernovianty, “The Level of People's Desire to Use Bike Paths in Pontianak,” JELAST: Journal of PWK, Marine, Civil, Mining, Tanjungpura University, 2018.
[9] J. Battle, Binary Logistic Regression, [online], Available, https://www.Juanbattle.com/Files/2012/04/Regression-Logistic-4.Pdf . Accessed: November 22, 2021.
[10] “Binary Logistic Regression,” Statistic Solution, 2021. [online], Available https://www.Statisticssolutions.com/Binary-Logistic-Regression. Accessed: November 22.
[11] Ana Beatriz Pereir Segadilhaa and Suely da Penha Sanches, “Identification of Factors That Influence Cyclists´ Route Choice,” Xi Congress of Transport Engineering (CIT), Procedia - Social and Behavioral Sciences, vol. 160, pp. 372 – 380, 2014. Crossef, https://doi.org/10.1016/j.sbspro.2014.12.149
[12] C. Hua, Y. J. Choi, and Q. Shi, “Binary Logistic Regression, Companion to BER 642,” Advanced Regression Methods, vol. 10, 2021. https://Bookdown.org/Chua/Ber642_Advanced_Regression/Binary-Logistic-Regression.html, Accessed: November 2, 2021.
[13] J. M. Hilbe, “Practical Guide to Logistic Regression,” CRC Press, Taylor & Francis Group, 2016.
[14] O. C. Puan, Y. A. H. Hassan, N. Mashers, M. K. Idham, N.A. Hassan, M. N. M. Warid, M. R. Hainan, “Transportation Mode Choice Binary Logit Model: A Case Study for Johor Bahru City,” IOP Conference Series: Materials Science and Engineering, vol. 527, no. 012066, 2019. Crossef, https://doi.org/10.1088/1757-899x/527/1/012066
[15] Dwi Novi Wulansari, and Milla Dwi Astari “Mode Choice Analysis Using the Discrete Choice Model From Transport User (Case Study: Jakarta LRT, Indonesia),” MATEC Web of Conferences, vol. 181, no. 03001, 2018.
[16] Manssour bin miskeen, et al., “ Modelling Intercity Transport Mode Choice Behavior in Libya: A Binary Logit Model for Business Trips By Private Car and Intercity Bus,” Australian Journal of Basic and Applied Sciences, vol. 7, no. 1, pp. 302-311, 2013.
[17] Vineet Chauhan, Hemant K. Suman and Nomesh B. Bolia, “Binary Logistic Model for Estimation of Mode Shift into Delhi Metro,” The Open Transportation Journal, vol. 10, pp. 124-136, 2013. Crossref, http://dx.doi.org/10.2174/1874447801610010124
[18] Sowjanya Dhulipala, Salini P S, and B. K. Katti, “Study of Impact of Various Attributes on Route Choice Through Logistic Regression Route Choice Modelling,” Conference: 2nd Conference on Transportation Systems Engineering and Management, NIT Tiruchirappalli India, 2015.
[19] Yetis Sazi Murat, and Nurcan Uludag, “Route Choice Modelling in Urban Transportation Networks Using Fuzzy Logic and Logistic Regression Methods,” Journal of Scientific and Industrial Research, vol. 67, no. 1, 2008.
[20] Tae Hyeong Gim, and Joonho Ko, “Maximum Likelihood and Firth Logistic Regression of the Pedestrian Route Choice,” International Regional Science Review, vol. 40, no. 6, pp.616-637, 2017. Crossref, http://dx.doi.org/10.1177/0160017615626214
[21] Socrates Basbas, V. Tetou, and I. Politis, ‘‘Ordinal and Binary Logistic Logit Models for Examining Behavioural, Infrastructure and Perception Factors Influencing Biking,” Wit Transactions on the Built Environment, vol. 130, pp. 573-584, 2013. Crossref, http://dx.doi.org/10.2495/UT130461
[22] AnaBarberan, Joãode Abreu e Silva, and Andres Monzon, “Factors Influencing Bicycle Use: A Binary Choice Model With Panel Data,” 20th Euro Working Group on Transportation Meeting, EWGT 2017, Transportation Research Procedia, vol. 27, pp. 253–260, 2017. Crossref, https://doi.org/10.1016/j.trpro.2017.12.097
[23] Tingting An, and Langong Hou, “Based on Binary Logit Model Analysis on Characteristics of Sharing Bicycle Travel Taking Fucheng District of Mianyang City as an Example,” Open Journal of Social Sciences, vol. 8, pp. 260-273, 2020. Crossref, https://doi.org/10.4236/jss.2020.84019
[24] Jerilee Pachoco Cameña, and Jun T. Castro, “Cycling Odds: Factors Affecting the Propensity to Use Bicycles in a Highly Urbanized City in the Philippines,” Philippine Transportation Journal, vol. 2, no. 2, 2019.
[25] Sareh Bahrololoom, William Young, and David Logan, “A Random Parameter Model of Factors Influencing Bicycle Fatal and Serious Injury Crashes in Victoria, Australia,” Proceedings of Australasian Transport Research Forum, 2017.
[26] S. U. A. Shofi, “An Empirical Analysis of Bike Safety in Lawrence Using Road Geometry and Traffic Characteristics, Master Thesis, Urban Planning, Geography and Atmospheric Science,” The Graduate Faculty of the University of Kansas, 2017.