Analysis of subway station capacity with the use of queueing theory |
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| Institution: | 1. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;1. University of Rome “La Sapienza”, Department of Civil, Building and Environmental Engineering, Transport Area, via Eudossiana 18, 00184 Rome, Italy;2. University of Pardubice, Jan Perner Transport Faculty, Department of Transport Technology and Control, Studentská 95, 532 10 Pardubice, Czech Republic;3. European Commission, Joint Research Centre (JRC), Institute for Prospective Technological Studies (JRC-IPTS), Edificio Expo, C/Inca Garcilaso 3, 41092 Seville, Spain;1. Transportation Research Group, Faculty of Engineering and the Environment, Southampton Boldrewood Innovation Campus, University of Southampton, Burgess Road, Southampton SO16 7QF, UK;2. Arup, 13 Fitzroy Street, London W1T 4BQ, UK;1. Department of Building Science, School of Architecture, Tsinghua University, Beijing, China;2. Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, China |
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| Abstract: | A new concept of subway station capacity (SSC) is defined according to the gathering and scattering process. A queuing network analytical model of station is created for calculating SSC, which is built by M/G/C/C state dependent queuing network and discrete time Markov chain (DTMC). Based on the definition and the analytical queuing network, a SSC optimization model is developed, whose objective function is to optimize SSC with a satisfactory rate of remaining passengers. Besides, a solution to the model is proposed integrating response surface methodology with iterative generalized expansion method (IGEM) and DTMC. A case study of Beijing Station in Beijing subway line 2 is implemented to verify the validity and practicability of the proposed methods by comparison with simulation model in different experiments. Finally, some sensitivity analysis results are provided to identify the nodes that have the greatest impact on SSC. |
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| Keywords: | Subway station capacity Response surface methodology Queuing network Remaining passengers Incoming passenger control |
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