TY - GEN
T1 - An Agent-Based Model for Evaluating the Boarding and Alighting Efficiency of Autonomous Public Transport Vehicles
AU - Su, Boyi
AU - Andelfinger, Philipp
AU - Eckhoff, David
AU - Cornet, Henriette
AU - Marinkovic, Goran
AU - Cai, Wentong
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067619101&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-22734-0_39
DO - 10.1007/978-3-030-22734-0_39
M3 - Conference contribution
AN - SCOPUS:85067619101
SN - 9783030227333
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 534
EP - 547
BT - Computational Science – ICCS 2019 - 19th International Conference, Proceedings
A2 - Rodrigues, João M.F.
A2 - Cardoso, Pedro J.S.
A2 - Monteiro, Jânio
A2 - Lam, Roberto
A2 - Krzhizhanovskaya, Valeria V.
A2 - Lees, Michael H.
A2 - Sloot, Peter M.A.
A2 - Dongarra, Jack J.
PB - Springer Verlag
T2 - 19th International Conference on Computational Science, ICCS 2019
Y2 - 12 June 2019 through 14 June 2019
ER -