skip to main content

Preferensi Pemilihan Lokasi Idle-Time Pengemudi Ojek Daring di Kota Bandung

*Maya Safira  -  Institut Teknologi Bandung, Indonesia
Azwan Nazamuddin  -  Institut Teknologi Bandung, Indonesia
Hafiyyan Hilmy Fawwaz  -  Institut Teknologi Bandung, Indonesia
Hanafi Kholifatul Iman  -  Institut Teknologi Bandung, Indonesia
Petrus Natalivan  -  Institut Teknologi Bandung, Indonesia
Ibnu Syabri  -  Institut Teknologi Bandung, Indonesia

Citation Format:
Abstract
Ojek daring, merupakan salah satu inovasi TIK yang terjadi pada bidang transportasi yang melayani kebutuhan perjalanan hingga pengantaran berbagai kebutuhan harian masyarakat. Penelitian ini membahas implikasi ojek daring berbasis Transport Super Applications (TSA) dalam transportasi perkotaan. Implikasi tersebut berkaitan dengan aktivitas waktu tunggu (idle-time) pengemudi yang menyebabkan masalah kemacetan, berkurangnya ketersediaan parkir, pemiihan moda transportasi publik yang menurun, dan penggunaan area publik yang mengganggu kenyamanan. Fokus pada dampak idle-time pengemudi yang mencari pesanan, studi ini menganalisis pola pergerakan dan pemilihan lokasi. Metode kuesioner dengan skala likert pada 244 pengemudi di Kota Bandung digunakan untuk mengumpulkan data persepsi pengemudi terhadap lokasi idle-time pada berbagai waktu. Hasil menunjukkan variasi karakteristik responden dan mengidentifikasi jam puncak layanan pada interval 06:00-08:00, 11:00-13:00, dan 16:00-21:00. Lokasi komersial seperti restoran, pasar, dan pusat perbelanjaan menjadi pilihan utama sepanjang hari. Hal ini sejalan dengan hasil analisis dari catatan perjalanan pengemudi yang dikumpulkan. Faktor operasional, termasuk penggunaan informasi potensi pesanan dan jam aktif pengemudi, terbukti signifikan dalam menentukan lokasi idle-time.
Fulltext View|Download
Keywords: Ojek Daring, Pemilihan Lokasi, Idle-Time, Preferensi, Kota Bandung

Article Metrics:

  1. Al Ayyubi, S. (2019, November 11). Ini Penyebab Ojek Online Sering Mangkal Sembarangan. Bisnis.com. https://jakarta.bisnis.com/read/20191111/77/1168895/ini-penyebab-ojek-online-sering-mangkal-sembarangan
  2. Alemi, F., Circella, G., Handy, S., & Mokhtarian, P. (2018). What Influences Travelers to Use Uber? Exploring the Factors Affecting the Adoption of on-Demand Ride Services in California. Travel Behaviour and Society, 13, 88–104. https://doi.org/10.1016/j.tbs.2018.06.002
  3. Bhandari, P. (2020, July 9). Descriptive statistics | Definitions, Types, Examples. Scribbr. https://www.scribbr.com/statistics/descriptive-statistics/
  4. Brown, A. (2019). Redefining Car Access: Ride-Hail Travel and Use in Los Angeles. Journal of the American Planning Association, 85(2), 83–95. https://doi.org/10.1080/01944363.2019.1603761
  5. Chakraborty, J., Pandit, D., Xia, J., & Chan, F. (2022). Modeling the Decision of Ridesourcing Drivers to Park and Wait at Trip Ends: a Comparison between Perth, Australia and Kolkata, India. Transportation. https://doi.org/10.1007/s11116-022-10367-9
  6. Chalermpong, S., Kato, H., Thaithatkul, P., Ratanawaraha, A., Fillone, A., Hoang-Tung, N., & Jittrapirom, P. (2023). Ride-Hailing Applications in Southeast Asia: a literature Review. International Journal of Sustainable Transportation, 17(3), 298–318. https://doi.org/10.1080/15568318.2022.2032885
  7. Clewlow, R. R., & Mishra, G. S. (2017). Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States. eScholarship. https://escholarship.org/uc/item/82w2z91j
  8. Deschaintres, E., Morency, C., & Trépanier, M. (2022). Cross-Analysis of the Variability of Travel Behaviors Using One-Day Trip Diaries and Longitudinal Data. Transportation Research Part A: Policy and Practice, 163, 228–246. https://doi.org/10.1016/j.tra.2022.07.013
  9. De Dios Ortúzar, J., & Willumsen, L. G. (2011). Modelling transport. John wiley & sons. DOI: 10.1002/9781119993308
  10. Fauzia, W., Setiadi, H., & Rizqihandari, N. (2022). Transformation of Public Space Utilization by Online Motorcycle Taxi. IOP Conference Series: Earth and Environmental Science, 1089(1), 012082. https://doi.org/10.1088/1755-1315/1089/1/012082
  11. Goodspeed, R., Xie, T., Dillahunt, T. R., & Lustig, J. (2019). An Alternative to Slow Transit, Drunk Driving, and Walking in Bad Weather: an Exploratory Study of Ridesourcing Mode Choice and Demand. Journal of Transport Geography, 79, 102481. https://doi.org/10.1016/j.jtrangeo.2019.102481
  12. Harpe, S. E. (2015). How to Analyze Likert and Other Rating Scale Data. Currents in Pharmacy Teaching and Learning, 7(6), 836–850. https://doi.org/10.1016/j.cptl.2015.08.001
  13. Kompas. (2022, November 26). Aplikasi Pesan Makanan "Online" Dorong Penjualan UMKM 1,9 Kali Lipat Dibanding "Offline". Kompas. https://money.kompas.com/read/2022/11/26/140000926/aplikasi-pesan-makanan-online-dorong-penjualan-umkm-1-9-kali-lipat-dibanding
  14. Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441
  15. Moore, D. S., McCabe, G. P., & Craig, B. A. (2009). Introduction to the Practice of Statistics (6th ed.). New York: W.H. Freeman and Company
  16. Rey, D., & Neuhäuser, M. (2011). Wilcoxon-Signed-Rank Test. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 1658–1659). Springer. https://doi.org/10.1007/978-3-642-04898-2_616
  17. Rizki, M., Joewono, T. B., Belgiawan, P. F., & Irawan, M. Z. (2021). The Travel Behaviour of Ride-Sourcing Users, and Their Perception of the Usefulness of Ride-Sourcing Based on the Users’ Previous Modes of Transport: a Case Study in Bandung City, Indonesia. IATSS Research, 45(2), 267–276. https://doi.org/10.1016/j.iatssr.2020.11.005
  18. Roa, L., Correa-Bahnsen, A., Suarez, G., Cortés-Tejada, F., Luque, M. A., & Bravo, C. (2021). Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications. Expert Systems with Applications, 169, 114486. https://doi.org/10.1016/j.eswa.2020.114486
  19. Rodrigue, J. P. (2020). The Geography of Transport Systems (5th ed.). Routledge. https://doi.org/10.4324/9780429346323
  20. Safira, M. (2022). The Multidimensional Impacts of Multi-Service Transport Platform (MSTP) on Activity-Travel Behavior and Urban Form: a Case of Jakarta, Indonesia. Hiroshima University. https://ir.lib.hiroshima-u.ac.jp/00051833
  21. Safira, M., & Chikaraishi, M. (2022). On the Empirical Association between Spatial Agglomeration of Commercial Facilities and Transportation Systems in Japan: a Nationwide Analysis. Journal of Transport and Land Use, 15(1). https://doi.org/10.5198/jtlu.2022.1968
  22. Salomon, I. (1986). Telecommunications and Travel Relationships: a Review. Transportation Research Part A: General, 20(3), 223–238. https://doi.org/10.1016/0191-2607(86)90096-8
  23. Statista. (2022). Number of Internet Users Worldwide 2022. Statista. https://www.statista.com/statistics/273018/number-of-internet-users-worldwide/
  24. Van Acker, V., Van Wee, B., & Witlox, F. (2010). When Transport Geography Meets Social Psychology: Toward a Conceptual Model of Travel Behaviour. Transport Reviews, 30(2), 219–240. https://doi.org/10.1080/01441640902943453
  25. Wang, H., & Yang, H. (2019). Ridesourcing Systems: a Framework and Review. Transportation Research Part B: Methodological, 129, 122–155. https://doi.org/10.1016/j.trb.2019.07.009
  26. Yadolah, D. (2008). Kruskal-Wallis test. In The Concise Encyclopedia of Statistics (pp. 288–290). Springer New York. https://doi.org/10.1007/978-0-387-32833-1_216
  27. Yu, H., & Peng, Z.-R. (2020). The Impacts of Built Environment on Ridesourcing Demand: a Neighbourhood Level Analysis in Austin, Texas. Urban Studies, 57(1), 152–175. https://doi.org/10.1177/0042098019828180

Last update:

No citation recorded.

Last update: 2024-12-23 09:50:08

No citation recorded.