Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic
DOI:
https://doi.org/10.56225/ijgoia.v2i4.262Keywords:
Fuzzy Quadratic Programming, Interval-Valued Fuzzy, Ride-Hailing, UncertaintyAbstract
Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.
References
Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Transportation Research Part B: Methodological, 45(9), 1450–1464. https://doi.org/10.1016/j.trb.2011.05.017
Anindhita, W., Arisanty, M., & Rahmawati, D. (2016). Analisis Penerapan Teknologi Komunikasi Tepat Guna Pada Bisnis Transportasi Ojek Online. Prosiding Seminar Nasional Indocompac Universitas Bakrie, 2, 712–729.
Azizah, A., & Adawia, P. R. (2018). Analisis perkembangan industri transportasi online di era inovasi disruptif (Studi Kasus PT Gojek Indonesia). Cakrawala: Jurnal Humaniora Bina Sarana Informatika, 18(2), 149–156.
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
Chandler, C. (2019). Grab vs. Go-Jek: Inside Asia's battle of the 'superapps'. Fortune. In Retrieved Dec (Vol. 20, p. 2020).
Cramer, J., & Krueger, A. B. (2016). Disruptive Change in the Taxi Business: The Case of Uber. American Economic Review, 106(5), 177–182. https://doi.org/10.1257/aer.p20161002
Crittenden, A. B., Crittenden, V. L., & Crittenden, W. F. (2017). Industry Transformation via Channel Disruption. Journal of Marketing Channels, 24(1–2), 13–26. https://doi.org/10.1080/1046669X.2017.1346974
Do, M., Byun, W., Shin, D. K., & Jin, H. (2019). Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method. Sustainability, 11(20), 5–615. https://doi.org/10.3390/su11205615
Flores, O., & Rayle, L. (2017). How cities use regulation for innovation: The case of Uber, Lyft and Sidecar in San Francisco. Transportation Research Procedia, 25, 3756–3768.
Guo, X., Caros, N. S., & Zhao, J. (2021). Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand. Transportation Research Part B: Methodological, 150(1), 161–189. https://doi.org/10.1016/j.trb.2021.05.015
Huidobro, P., Alonso, P., Janiš, V., & Montes, S. (2022). Convexity and level sets for interval-valued fuzzy sets. Fuzzy Optimization and Decision Making, 21(4), 553–580. https://doi.org/10.1007/s10700-021-09376-7
Luo, Y., Jia, X., Fu, S., & Xu, M. (2019). pRide: Privacy-Preserving Ride Matching Over Road Networks for Online Ride-Hailing Service. IEEE Transactions on Information Forensics and Security, 14(7), 1791–1802. https://doi.org/10.1109/TIFS.2018.2885282
Lyu, G., Cheung, W. C., Teo, C.-P., & Wang, H. (2019). Multi-Objective Online Ride-Matching. SSRN Electronic Journal, 13(7), 2–47. https://doi.org/10.2139/ssrn.3356823
Megantara, T. R., Supian, S., & Chaerani, D. (2022). Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty. Sustainability, 14(17), 10–648. https://doi.org/10.3390/su141710648
Nandi. (2019). The Influence of Online Transportation Application to the Mobility and Economic of the Society (Case Study on Using Grab and Go-Jek in Bandung, Indonesia). IOP Conference Series: Earth and Environmental Science, 286(1), 012034. https://doi.org/10.1088/1755-1315/286/1/012034
Qin, X., Yang, H., Wu, Y., & Zhu, H. (2021). Multi-party ride-matching problem in the ride-hailing market with bundled option services. Transportation Research Part C: Emerging Technologies, 131(1), 103–287. https://doi.org/10.1016/j.trc.2021.103287
Stiglic, M., Agatz, N., Savelsbergh, M., & Gradisar, M. (2015). The benefits of meeting points in ride-sharing systems. Transportation Research Part B: Methodological, 82(1), 36–53. https://doi.org/10.1016/j.trb.2015.07.025
Su, J.-S. (2007). Fuzzy programming based on interval-valued fuzzy numbers and ranking. International Journal Contemporary Mathematical Sciences, 2(8), 393–410.
Wibawa, B. M., Rahmawati, Y., & Rainaldo, M. (2018). Analisis Industri Bisnis Jasa Online Ride Sharing di Indonesia. Jurnal Bisnis Dan Manajemen, 8(1), 9–20.
Xu, Y., Wang, W., Xiong, G., Liu, X., Wu, W., & Liu, K. (2022). Network-Flow-Based Efficient Vehicle Dispatch for City-Scale Ride-Hailing Systems. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5526–5538. https://doi.org/10.1109/TITS.2021.3054893
Yan, C., Zhu, H., Korolko, N., & Woodard, D. (2020). Dynamic pricing and matching in ride‐hailing platforms. Naval Research Logistics (NRL), 67(8), 705–724. https://doi.org/10.1002/nav.21872
Yang, H., Qin, X., Ke, J., & Ye, J. (2020). Optimizing matching time interval and matching radius in on-demand ride-sourcing markets. Transportation Research Part B: Methodological, 131, 84–105. https://doi.org/10.1016/j.trb.2019.11.005
Young, M., & Farber, S. (2019). The who, why, and when of Uber and other ride-hailing trips: An examination of a large sample household travel survey. Transportation Research Part A: Policy and Practice, 119(1), 383–392. https://doi.org/10.1016/j.tra.2018.11.018
Yu, H., Jia, X., Zhang, H., & Shu, J. (2022). Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance in Online Ride Hailing Services. IEEE Transactions on Services Computing, 15(4), 1841–1854. https://doi.org/10.1109/TSC.2020.3022875
Yu, H., Shu, J., Jia, X., Zhang, H., & Yu, X. (2019). lpRide: Lightweight and Privacy-Preserving Ride Matching Over Road Networks in Online Ride Hailing Systems. IEEE Transactions on Vehicular Technology, 68(11), 10418–10428. https://doi.org/10.1109/TVT.2019.2941761
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright @2022. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted to copy and redistribute the material in any medium or format, remix, transform, and build upon the material for any purpose, even commercially.
This work is licensed under a Creative Commons Attribution 4.0 International License.