Simulation of delayed buses in a dumpling state and analysis of maximum waiting time using logistic regression

• Autorzy: Tanamoto Tetsufumi , Sekiguchi Haruya , Orimo Souta , Kawakami Taisei

Identyfikatory

DOI

10.20858/tp.2023.18.2.15

• Języki publikacji: EN

Abstrakty

EN

As cities become larger and societies become more complicated, the corresponding transportation systems also become more complicated. Thus far, many important transportation models have been investigated and applied to societies. In this work, we analyze a bus transportation model that includes high randomness. By strengthening the viewpoint of the users, the bunching of buses is further explored and considered as “the dumpling bus state,” referring to cases when the next scheduled buses closely run behind a delayed bus for a while. It is described that waiting people are split into winners (people with shorter waiting times) and losers (people with longer waiting times). Waiting time is also analyzed using logistic regression to obtain the probability of people who continue to wait.

Słowa kluczowe

EN

bus transportation; logistic regression; frustration

PL

transport autobusowy; regresja logistyczna; udaremnienie

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2023
• Tom: T. 18, z. 2
• Strony: 171--180
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Tanamoto Tetsufumi

• Teikyo University; 1-1 Toyosatodai, Utsunomiya, Tochigi 320-8551, Japan

• https://orcid.org/0000-0002-1373-2812

autor

Sekiguchi Haruya

• Teikyo University; 1-1 Toyosatodai, Utsunomiya, Tochigi 320-8551, Japan

• https://orcid.org/0009-0007-9791-2936

autor

Orimo Souta

• Teikyo University; 1-1 Toyosatodai, Utsunomiya, Tochigi 320-8551, Japan

• https://orcid.org/0009-0007-8631-4244

autor

Kawakami Taisei

• Teikyo University; 1-1 Toyosatodai, Utsunomiya, Tochigi 320-8551, Japan

• https://orcid.org/0009-0001-8673-0076

Bibliografia

• 1. Sakai, K. Public transport promotion and Mobility-as-a-Service. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2020. Vol. E103. A. P. 226-230.
• 2. Kalašová, A. & Krchová, Z. The possibility of solving cycling transport in central urban areas. Transport Problems. 2011. Vol. 6. No. 2. P. 21-26.
• 3. Pshinko, O. & Charkina, T. & Martseniuk, L. & & Orlovska, O. Hubs as a key tool for improving the quality of the service and development of multimodal passenger traffic. Transport Problems. 2022. Vol. 17. No. 1. P. 201-214.
• 4. Kadam, A.J. & Patil, V. & Kaith, K. & Patil, D. & Sham. Developing a smart bus for smart city using IOT technology. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). 2018. P. 1138-1143.
• 5. Ghareeb, M. & Ghamlous, A. & Hamdan, H. & Bazzi, A. & Abdul-Nabi, S. Smart bus: A tracking system for school buses. In: Sensors Networks Smart and Emerging Technologies (SENSET). 2017. P. 1-3.
• 6. Ram, S. & Wang, Y. & Currim, F. & Dong, F. & Dantas, E. & Sabóia, L.A. SMARTBUS: A web application for smart urban mobility and transportation. In: Proceedings of the 25th International Conference Companion on World Wide Web. 2016. P. 363-368.
• 7. Kang, L. & Poslad, S. & Wang, W. & Li, X. & Zhang, Y. & Wang, C. A public transport bus as a flexible mobile smart environment sensing platform for IoT. In: 2016 12th International Conference on Intelligent Environments (IE). P. 1-8.
• 8. Fong, S.L. & Yung, D.C.W. & Ahmed, F.Y.H. & Jamal, A. Smart city bus application with Quick Response (QR) code payment. In: Proceedings of the 2019 8th International Conference on Software and Computer Applications (ICSCA). 2019. P. 248-252.
• 9. The concept of the metropolitan MaaS “We! Tokyo MaaS”. Available at: https://www.tokyometro.jp/maas/
• 10. Nagatani, T. The physics of traffic jams. Reports on progress in physics. 2002. Vol. 65. P. 1331.
• 11. Helbing, D. & Hennecke, A. & Shvetsov, V. & Treiber, M. Micro- and macro-simulation of freeway traffic. Mathematical and Computer Modelling. 2002. Vol. 35. P. 517-547.
• 12. Schönhof, M. & Helbing, D. Empirical features of congested traffic states and their implications for traffic modeling. Transportation Science. 2007. Vol. 41. P. 135-166.
• 13. Sugiyama, Y. & Fukui, M. & Kikuchi, M. & Hasebe, K. & Nakayama, A. & Nishinari, K. & Tadaki, S. & Yukawa, S. Traffic jams without bottlenecks - experimental evidence for the physical mechanism of the formation of a jam. New J. Phys. 2008. Vol. 10. No. 033001.
• 14. Bando, M. & Hasebe, K. & Nakayama, A. & Shibata, A. & Sugiyama, Y. Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E. 1995. Vol. 51. P. 1035-1042.
• 15. Saw, VL. & Chung, N.N. & Quek, W.L. & Pang, Y.E.I. & Chew. L.Y. Bus bunching as a synchronisation phenomenon. 2019. Sci Rep. Vol. 9. No. 6887.
• 16. Luo, Y.J. & Jia, B. & Li, X.G. & Wang, C. & Gao, Z.Y. A realistic cellular automata model of bus route system based on open boundary. Transportation Research Part C: Emerging Technologies. 2012. Vol. 25. P. 202-213.
• 17. Kerner, B.S. Experimental features of self-organization in traffic flow. Phys. Rev. Lett. 1988. Vol. 81. P. 3797-3800.
• 18. Kerner, B.S. Physics of Automated-Driving Vehicular Traffic. arXiv:2303.17733. 2023.
• 19. Baghbani, A. & Bouguila, N. & & Patterson, Z. Short-term passenger flow prediction using a bus network graph convolutional long short-term memory neural network model. Transportation Research Record. 2023. Vol. 2677. P. 1331-1340.
• 20. Taylor, S. Waiting for Service: The relationship between delays and evaluations of service. J. Marketing. 1994. Vol. 58. P. 56-69.