Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study

•Introduce a problem how to passenger flow control under uncertain demand.•Propose an analysis framework of subway station service capacity for passenger flow control.•Develop a simulation-based algorithm embedded genetic algorithm and data envelopment analysis to solve these models.•Provide a real-...

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Veröffentlicht in:Transportation research. Part E, Logistics and transportation review Jg. 87; S. 130 - 148
Hauptverfasser: Xu, Xin-yue, Liu, Jun, Li, Hai-ying, Jiang, Man
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Exeter Elsevier India Pvt Ltd 01.03.2016
Elsevier Sequoia S.A
Schlagworte:
Airports
Algorithms
Case studies
Data envelopment analysis
Demand
Flow control
Genetic algorithm
Genetic algorithms
Mathematical models
Passengers
Simulation
Simulation optimization
Station service capacity
Stations
Studies
Subway station
Subway stations
Subways
Transportation
Uncertain demand
Subway station
Uncertain demand
Genetic algorithm
Simulation optimization
Station service capacity
Data envelopment analysis
ISSN:1366-5545, 1878-5794
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Zusammenfassung:•Introduce a problem how to passenger flow control under uncertain demand.•Propose an analysis framework of subway station service capacity for passenger flow control.•Develop a simulation-based algorithm embedded genetic algorithm and data envelopment analysis to solve these models.•Provide a real-world large-scale case study with detailed analysis results. This paper proposes a problem of passenger flow organization in subway stations under uncertain demand. The existing concepts of station service capacity are extended and further classified into three in different demand scenarios. Mathematical models are put forward to measure the three capacities and a unified simulation-based algorithm is developed to solve them. To increase computing speed, data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm. A case study will demonstrate the performance of the proposed algorithm and give a detailed procedure of passenger flow control based on station service capacity in various demand scenarios.
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ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2016.01.004