Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v38i4.1191
Multi-Objective Optimisation of Timetable for Urban Rail Transit during the End-of-Operation Period for Large-Scale Events
Xiaomei XIA
; Business School, University of Shanghai for Science and Technology, Shanghai, China
Shengqian WU
; Business School, University of Shanghai for Science and Technology, Shanghai, China
Tianyi ZHANG
; Business School, University of Shanghai for Science and Technology, Shanghai, China
Jiao YAO
; Business School, University of Shanghai for Science and Technology, Shanghai, China
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* Dopisni autor.
Sažetak
This study proposes a timetable multi-objective optimisation model for urban rail transit during the end-of-operation period, addressing dynamic passenger demand under limited train capacity. The model simultaneously minimises the unreachable rate of last-train origin-destination (OD) points and passenger flow-induced congestion at the station. Key decisions involve optimising dwell times and headways while incorporating dynamic transfer spatiotemporal constraints and train capacity limitations. To resolve the computational complexity from non-convex and non-linear terms, a multi-objective whale optimisation algorithm is employed. Case studies under large-scale events reveal that moderately extending dwell times (1.5-2 minutes) alleviates congestion, while excessive extensions prove ineffective due to line length limitations. For headways, 8-10 minutes generally optimises departure frequency, though specific Pareto-optimal solutions may flexibly adopt 4-8 minute intervals. The findings enable operators to balance accessibility and congestion mitigation based on event characteristics, enhancing operational efficiency and passenger satisfaction. The research provides actionable strategies for improving last-train transfer coordination and station flow management during special operational periods.
Ključne riječi
timetable multi-objective optimisation; end-of-operation period; large-scale events; unreachable rate of last train origin-destination (OD) points; passenger flow-induced congestion at the station; whale optimisation algorithm
Hrčak ID:
346676
URI
Datum izdavanja:
28.4.2026.
Posjeta: 20 *