Analysis of Transportation Selection for Travel Work

Abdillah Arif Nasution 1  and Keulana Erwin 1
  • 1 University of Sumatera Utara, Department of Accounting, Medan, Indonesia

Abstract

The paper discusses the optimizing possibilities in terms of use of public transportation that is very necessary considering the difficulty of increasing the capacity of the road with widen road infrastructure in an effort to manage “supply”. Therefore, an alternative approach when managing “demand” for transportation system can be controlled. This is especially needed in settlements newly developed rapidly in Deli Serdang Regency, namely in the Galang Region. The region Galang area with a population of 613 working people with details of 189 civil servants and 424 private employees who the majority (94%) use private transportation. One aspect that is studied within this manuscript is the amount of transportation costs of travel to work using private transportation (motorcycle) and public transportation (angkot or mikrolet). Transportation selection modeling is done using the Bi-nomial Model Binary Logit. Based on the analysis of the results obtained, it can be concluded that, if the difference in transportation costs with private transport getting bigger, the opportunities to use this mode will increase. The balance between the costs and using private and public transport is maintained if the costs of private transport are 1.4 times greater than the cost public transportation.

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  • [1] Tamin O.Z. (2003), Perencanaan dan Pemodelan Transportasi, Penerbit ITB Bandung.

  • [2] Sugiyono. (2012). Metode Penelitian Kuantitatif & Kualitatif. Bandung, Indonesia: Alfabeta.

  • [3] Kuncoro, M. (2014). Metode Riset untuk Bisnis dan Ekonomi, 4th edition, Jakarta, Indonesia: Erlangga.

  • [4] Nasution, A.A., Azmi, Z., Siregar, I. & Erlina, I. (2018). Impact of air transport on the Indonesian economy. MATEC Web of Conferences. 236. DOI: 10.1051/matecconf/.

  • [5] Engebrethsen, E. & Dauzère-Pérès, S. (2019). Transportation mode selection in inventory models: A literature review. European Journal of Operational Research. 279(1), 1-25. DOI:10.1016/j.ejor.2018.11.067.

  • [6] Frank, S., Berk, S., Hernandez, L., Hogarth, P., Shill, H.A., Siddiqi, B. & Simon, D.K. (2019). Transportation innovation to aid Parkinson disease trial recruitment. Contemporary Clinical Trials Communications. 16. DOI:10.1016/j.conctc.2019.100449.

  • [7] Khordagui, N. (2019). Parking prices and the decision to drive to work: Evidence from California. Transportation Research Part A: Policy and Practice. 130, 479-495. DOI:10.1016/j.tra.2019.09.064.

  • [8] McCormack, G.R., Koohsari, M.J., Oka, K., Friedenreich, C.M., Blackstaffe, A., Alaniz, F.U. & Farkas, B. (2019). Differences in transportation and leisure physical activity by neighborhood design controlling for residential choice. Journal of Sport and Health Science. 8(6), 532-539. DOI:10.1016/j.jshs.2019.05.004.

  • [9] Zhang, X. & Huang, H. (2019). Vehicle classification based on feature selection with anisotropic magnetoresistive sensor. IEEE Sensors Journal. 19(21), 9976-9982. DOI:10.1109/JSEN.2019.2928828.

  • [10] Jianchuan, X. (2010). Modeling the generation and organization of household non-work activity: A case study of Beijing. In International Conference on Optoelectronics and Image Processing, ICOIP 2010, 2, 8-11. DOI:10.1109/ICOIP.2010.32.

  • [11] Liu, Y. & Li, Y. (2018). Characteristic variables and behavior analysis of simple and complex non-work trip chains base on binary logit model. In CICTP 2017: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation; Proceedings of the 17th COTA International Conference of Transportation Professionals, 2018-January, 3811-3821.

  • [12] Xianyu, J. & Juan, Z. (2011). Generation and organization of household non-work activity stops. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology. 11(4), 124-128.

  • [13] Sahu, P. K., Sharma, G. & Guharoy, A. (2018). Commuter travel cost estimation at different levels of crowding in a suburban rail system: A case study of Mumbai. Public Transport. 10(3), 379-398. DOI: 10.1007/s12469-018-0190-6.

  • [14] BARTUŠKA, Ladislav, Karel JEŘÁBEK a Li CHENGUANG. Determination of Traffic Patterns on urban roads. Communications, Žilina: Žilinská univerzita v Žilině, EDIS, 2017, roč. 19, č. 2, s. 103-108. ISSN 1335-4205.

  • [15] HANZL, Jiří, Ladislav BARTUŠKA, Elena ROZHANSKAYA a Petr PRŮŠA. Application of Floyd’s Algorithm on Transport Network of South Bohemian Region. Communications : scientific letters of the University of Žilina, Žilina: The University of Žilina, 2016, Volume 18, č. 2, s. 68-71. ISSN 1335-4205.

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