Advertisement

An Agent-Based Model for Evaluating the Boarding and Alighting Efficiency of Autonomous Public Transport Vehicles

  • Boyi Su
  • Philipp AndelfingerEmail author
  • David Eckhoff
  • Henriette Cornet
  • Goran Marinkovic
  • Wentong Cai
  • Alois Knoll
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11536)

Abstract

A key metric in the design of interior layouts of public transport vehicles is the dwell time required to allow passengers to board and alight. Real-world experimentation using physical vehicle mock-ups and involving human participants can be performed to compare dwell times among vehicle designs. However, the associated costs limit such experiments to small numbers of trials. In this paper, we propose an agent-based simulation model of the behavior of passengers during boarding and alighting. High-level strategical behavior is modeled according to the Recognition-Primed Decision paradigm, while the low-level collision-avoidance behavior relies on an extended Social Force Model tailored to our scenario. To enable successful navigation within the confined space of the vehicle, we propose a mechanism to emulate passenger turning while avoiding complex geometric computations. We validate our model against real-world experiments from the literature, demonstrating deviations of less than 11%. In a case study, we evaluate the boarding and alighting times required by three autonomous vehicle interior layouts proposed by industrial designers.

References

  1. 1.
    Transportation – Logistics and Services – Public Passenger Transport – Service Quality Definition, Targeting and Measurement. Standard EN 13816:2002, European Committee for Standardization (2002)Google Scholar
  2. 2.
    Alonso-Marroquin, F., Busch, J., Chiew, C., Lozano, C., Ramírez-Gómez, A.: Simulation of counterflow pedestrian dynamics using spheropolygons. Phys. Rev. E 90, 063305 (2014)CrossRefGoogle Scholar
  3. 3.
    Andelfinger, P., et al.: Incremental calibration of seat selection preferences in agent-based simulations of public transport scenarios. In: Proceedings of the Winter Simulation Conference (WSC), Gothenburg, Sweden, pp. 833–844, December 2018Google Scholar
  4. 4.
    Best, A., Narang, S., Manocha, D.: Real-time reciprocal collision avoidance with elliptical agents. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 298–305, May 2016Google Scholar
  5. 5.
    Bian, B., Zhu, N., Ling, S., Ma, S.: Bus service time estimation model for a curbside bus stop. Transp. Res. Part C Emerg. Technol. 57, 103–121 (2015)CrossRefGoogle Scholar
  6. 6.
    Bode, N.W.F., Wagoum, A.U.K., Codling, E.A.: Human responses to multiple sources of directional information in virtual crowd evacuations. J. R. Soc. Interface 11(91), 20130904 (2014)CrossRefGoogle Scholar
  7. 7.
    Cai, W.,et al.: COSMOS: CrOwd simulation for military OperationS. Technical report, School of Computer Eng., Nanyang Technological University, Singapore, July 2010Google Scholar
  8. 8.
    Connell, B.R., et al.: The Principles of Universal Design (1997). https://projects.ncsu.edu/www/ncsu/design/sod5/cud/about_ud/udprinciplestext.htm
  9. 9.
    Curtis, S., Guy, S.J., Zafar, B., Manocha, D.: Virtual Tawaf: a case study in simulating the behavior of dense, heterogeneous crowds. In: International Conference on Computer Vision Workshops, Barcelona, Spain, pp. 128–135, November 2011DGoogle Scholar
  10. 10.
    Curtis, S., Zafar, B., Gutub, A., Manocha, D.: Right of way: asymmetric agent interactions in crowds. Vis. Comput. 29(12), 1277–1292 (2013)CrossRefGoogle Scholar
  11. 11.
    Fernández, R., Zegers, P., Weber, G., Tyler, N.: Influence of platform height, door width, and fare collection on bus dwell time: laboratory evidence for Santiago de Chile. Transp. Res. Rec. J. Transp. Res. Board 2143, 59–66 (2010)CrossRefGoogle Scholar
  12. 12.
    Fletcher, D., Harrison, R., Karmakharm, T., Nallaperuma, S., Richmond, P.: RateSetter: roadmap for faster, safer, and better platform train interface design and operation using evolutionary optimisation. In: Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, pp. 1230–1237. ACM, July 2018Google Scholar
  13. 13.
    Gao, Y., Luh, P.B., Zhang, H., Chen, T.: A modified social force model considering relative velocity of pedestrians. In: International Conference on Automation Science and Engineering (CASE), Wisconsin, USA, pp. 747–751, August 2013Google Scholar
  14. 14.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)CrossRefGoogle Scholar
  15. 15.
    Klein, G.: The recognition-primed decision (RPD) model: looking back, looking forward. Nat. Decis. Mak., 285–292 (1997)Google Scholar
  16. 16.
    Langston, P.A., Masling, R., Asmar, B.N.: Crowd dynamics discrete element multi-circle model. Saf. Sci. 44(5), 395–417 (2006)CrossRefGoogle Scholar
  17. 17.
    Luo, L., Zhou, S., Cai, W., Lees, M., Low, M.Y.H., Sornum, K.: HumDPM: a decision process model for modeling human-like behaviors in time-critical and uncertain situations. In: Gavrilova, M.L., Tan, C.J.K., Sourin, A., Sourina, O. (eds.) Transactions on Computational Science XII. LNCS, vol. 6670, pp. 206–230. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22336-5_11CrossRefGoogle Scholar
  18. 18.
    Narang, S., Best, A., Manocha, D.: Interactive simulation of local interactions in dense crowds using elliptical agents. J. Stat. Mech. Theory Exp. 2017(3) (2017). Article number: 033403CrossRefGoogle Scholar
  19. 19.
    Perkins, A., Ryan, B., Siebers, P.O.: Modelling and simulation of rail passengers to evaluate methods to reduce dwell times. In: 14th International Conference on Modeling and Applied Simulation, MAS 2015, Rende, Italy, pp. 132–141 (2015)Google Scholar
  20. 20.
    Seriani, S., Fernandez, R.: Pedestrian traffic management of boarding and alighting in metro stations. Transp. Res. Part C Emerg. Technol. 53, 76–92 (2015)CrossRefGoogle Scholar
  21. 21.
    Sun, L., Tirachini, A., Axhausen, K.W., Erath, A., Lee, D.H.: Models of bus boarding and alighting dynamics. Transp. Res. Part A Policy Pract. 69, 447–460 (2014)CrossRefGoogle Scholar
  22. 22.
    Zhang, Q., Han, B., Li, D.: Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transp. Res. Part C Emerg. Technol. 16(5), 635–649 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Boyi Su
    • 1
    • 2
  • Philipp Andelfinger
    • 1
    • 2
    Email author
  • David Eckhoff
    • 1
    • 3
  • Henriette Cornet
    • 1
  • Goran Marinkovic
    • 1
  • Wentong Cai
    • 2
  • Alois Knoll
    • 2
    • 3
  1. 1.TUMCREATE Ltd.SingaporeSingapore
  2. 2.Nanyang Technological UniversitySingaporeSingapore
  3. 3.Technische Universität MünchenMunichGermany

Personalised recommendations