EXPLORING THE RESEARCH LANDSCAPE OF DRIVERLESS PUBLIC TRANSIT: A BIBLIOMETRIC ANALYSIS BASED ON WEB OF SCIENCE (2013–2024)
Article Sidebar
Main Article Content
Abstract
As urbanization accelerates, the traditional public transit model has undergone significant transformation, and driverless public transit has attracted attention from practitioners and researchers. However, a systematic review of the applications of driverless public transit has yet to be conducted. Through bibliometric analysis, this study systematically analyzes the current research status, hot topics, and future tendencies regarding driverless vehicles in public transit areas, based on 125 relevant articles collected from the Web of Science database. Firstly, through keyword co-occurrence analysis, four major research clusters are identified, i.e., optimization scheduling, user acceptance, public demand preference, and technical framework. In addition, the results show that China and the United States are the leading contributors, with international collaboration steadily increasing. Finally, this study analyzes the technological advances, social influences, and policy challenges of the future development of driverless vehicles in public transit areas and proposes strategies to promote their widespread application. These findings advance knowledge in the field of driverless vehicles and provide implications for policy formulation, technological innovation, and the sustainable development of driverless public transit.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้ ไม่ใช่ความคิดเห็นและความรับผิดชอบของผู้จัดทำ บรรณาธิการ กองบรรณาธิการ และคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี ความรับผิดชอบด้านเนื้อหาและการตรวจร่างบทความแต่ละเรื่องเป็นความคิดเห็นของผู้เขียนบทความแต่ละท่าน
References
Abe, R. (2019). Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems. Transportation Research Part A: Policy and Practice, 126, 94–113. https://doi.org/10.1016/j.tra.2019.06.003
Bae, I., Moon, J., & Seo, J. (2019). Toward a comfortable driving experience for a self-driving shuttle bus. Electronics, 8(9), Article 943. https://doi.org/10.3390/electronics8090943
Cao, Z., & Ceder, A. A. (2019). Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic. Transportation Research Part C: Emerging Technologies, 102, 370–395. https://doi.org/10.1016/j.trc.2019.03.018
Cheng, Y. H., & Lai, Y. C. (2024). Exploring autonomous bus users’ intention: Evidence from positive and negative effects. Transport Policy, 146, 91–101. https://doi.org/10.1016/j.tranpol.2023.11.004
Dai, Z., Liu, X. C., Chen, X., & Ma, X. (2020). Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies, 114, 598–619. https://doi.org/10.1016/j.trc.2020.03.001
Dong, X., DiScenna, M., & Guerra, E. (2019). Transit user perceptions of driverless buses. Transportation, 46(1), 35–50. https://doi.org/10.1007/s11116-017-9786-y
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Greenblatt, J. B., & Saxena, S. (2015). Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature Climate Change, 5(9), 860–863. https://doi.org/10.1038/nclimate2685
Hasan, U., Whyte, A., & AlJassmi, H. (2022). A microsimulation modelling approach to quantify environmental footprint of autonomous buses. Sustainability, 14(23), 1–31. https://doi.org/10.3390/su142315657
Herrenkind, B., Brendel, A. B., Nastjuk, I., Greve, M., & Kolbe, L. M. (2019). Investigating end-user acceptance of autonomous electric buses to accelerate diffusion. Transportation Research Part D: Transport and Environment, 74, 255–276. https://doi.org/10.1016/j.trd.2019.08.003
Hu, Q., Hu, S., Shen, S., Ouyang, Y., & Chen, X. M. (2024). Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities. Sustainable Cities and Society, 111, Article 105557. https://doi.org/10.1016/j.scs.2024.105557
Jo, A., Kwon, W., & Yi, K. (2024). Motion planning for urban autonomous bus considering localization uncertainty using offset-free MPC. IEEE Access, 12, 11743–11760. https://doi.org/10.1109/ACCESS.2024.3355205
Klinkhardt, C., Kandler, K., Kostorz, N., Heilig, M., Kagerbauer, M., & Vortisch, P. (2024). Integrating autonomous busses as door-to-door and first-/last-mile service into public transport: Findings from a stated choice experiment. Transportation Research Record, 2678(2), 605–619. https://doi.org/10.1177/03611981231175900
Li, H., Li, S., & Zhang, X. (2023). Coordination optimization of real-time signal priority of self-driving buses at arterial intersections considering private vehicles. Applied Sciences, 13(19), 1–15. https://doi.org/10.3390/app131910803
Liu, S., Huang, W., Ji, H., & Wang, L. (2024). Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system. Science Progress, 107(2), 1–37. https://doi.org/10.1177/00368504241249617
Lokhandwala, M., & Cai, H. (2018). Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC. Transportation Research Part C: Emerging Technologies, 97, 45–60. https://doi.org/10.1016/j.trc.2018.10.007
Mao, C., Liu, Y., & Shen, Z. J. M. (2020). Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 115, Article 102626. https://doi.org/10.1016/j.trc.2020.102626
Oliveira, R., Lima, P., Cirillo, M., & Wahlberg, B. (2021). Autonomous bus driving: A novel motion-planning approach. IEEE Vehicular Technology Magazine, 16(3), 29–37. https://doi.org/10.1109/MVT.2021.3086438
Rosell, J., & Allen, J. (2020). Test-riding the driverless bus: Determinants of satisfaction and reuse intention in eight test-track locations. Transportation Research Part A: Policy and Practice, 140, 166–189. https://doi.org/10.1016/j.tra.2020.08.013
Salonen, A. O. (2018). Passenger's subjective traffic safety, in-vehicle security and emergency management in the driverless shuttle bus in Finland. Transport Policy, 61, 106–110. https://doi.org/10.1016/j.tranpol.2017.10.011
Schandl, F., Lermer, E., & Hudecek, M. F. (2024). If it concerns me: An experimental investigation of the influence of psychological distance on the acceptance of autonomous shuttle buses. Collabra: Psychology, 10(1), Article 118770. https://doi.org/10.1525/collabra.118770
Shi, M., He, H., Li, J., Han, M., & Zhou, N. (2022). Path planning and following control of autonomous bus under time-varying parameters against parametric uncertainties and external disturbances. IEEE Transactions on Vehicular Technology, 71(7), 7057–7070. https://doi.org/10.1109/TVT.2022.3170440
Van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111, 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
Wang, L., Xue, X., Zhao, Z., & Wang, Z. (2018). The impacts of transportation infrastructure on sustainable development: Emerging trends and challenges. International Journal of Environmental Research and Public Health, 15(6), 1–15. https://doi.org/10.3390/ijerph15061172
Xie, H., David, A., Mamun, M. R. A., Prybutok, V. R., & Sidorova, A. (2022). The formation of initial trust by potential passengers of self-driving taxis. Journal of Decision Systems, 32(2), 326–355. https://doi.org/10.1080/12460125.2021.2023258
Zhou, X., Wu, L., Zhang, Y., Chen, Z. S., & Jiang, S. (2023). A robust deep reinforcement learning approach to driverless taxi dispatching under uncertain demand. Information Sciences, 646, 1–16. https://doi.org/10.1016/j.ins.2023.119401
