Width Design of Urban Rail Transit Station Walkway: A Novel Simulation-Based Optimization Approach
Afaq Khattak , Jiang Yangsheng , Hu Lu , Zhu Juanxiu
Urban Rail Transit ›› 2017, Vol. 3 ›› Issue (2) : 112 -127.
Width Design of Urban Rail Transit Station Walkway: A Novel Simulation-Based Optimization Approach
The optimal design of the walkway at an urban rail transit station is a vital issue. The Transit Capacity and Quality of Service Manual (TCQSM) TCRP-100 report for the design of urban rail transit station walkway and the existing design models neglect the important factors such as randomness in the passenger arrival rate, randomness and state-dependent service time of the walkway and blocking phenomenon when the passenger flow demand exceeds the walkway capacity. There obviously exists a need to develop a design approach that overcomes these shortcomings. For this purpose, this paper details a simulation-based optimization approach that provides width design through automatic reconfiguration of walkway width during the simulation–optimization process based on phase-type (PH) distribution. The integrated PH/PH(n)/C/C discrete-event simulation (DES) model and optimization method that uses the genetic algorithm (GA) work together concurrently to obtain optimized (design) widths for different passenger flow and level of service (LOS) The numerical experiments are conducted to compare the proposed model with the existing design methods. It reveals that: (1) The width obtained by our proposed model is higher than the existing width design models; (2) when squared coefficient of variation of passenger arrival interval increases, the walkway width increases more for our proposed model than the existing design models; (3) when the arrival rate increases, the walkway width of our proposed model increases faster than the existing design models; (4) the increase in the length of walkway has no significant effect on the walkway width.
Urban rail transit station / Walkway simulation-based optimization / Queueing system / PH distribution / Genetic Algorithm
| [1] |
Kittelson, Associates, U. S. F. T. Administration, T. C. R. Program, T. D. Corporation, and N. R. C. T. R. Board (2003) Transit capacity and quality of service manual. Transportation Research Board of the National Academies |
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
Miyazawa M, Sakuma Y, Yamaguchi S (2007) Asymptotic behaviors of the loss probability for a finite buffer queue with QBD structure. Stoch Models 23(1):79–95 |
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
Jiang Y, Zhu J, Hu L, Lin X, Khattak A (2015) A G/G(n)/C/C state-dependent simulation model for metro station corridor width design. J Adv Transp 50(3):273–295 |
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
Hagendorf O, Pawletta T, Larek R (2013) An approach for simulation-based parameter and structure optimization of MATLAB/Simulink models using evolutionary algorithms. SIMULATION |
| [23] |
|
| [24] |
Swisher JR, Hyden PD, Jacobson SH, Schruben LW (2000) Simulation optimization: a survey of simulation optimization techniques and procedures. In: Presented at proceedings of the 32nd conference on Winter simulation, Orlando, Florida |
| [25] |
|
| [26] |
|
| [27] |
Jiang Y, Lin X (2013) Simulation and optimization of the ticket vending machine configuration in metro stations based on anylogic software. In: Fourth international conference on transportation engineering, pp 754–760 |
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
Yan J, Wen J, Li L (2014) Genetic Algorithm Based Optimization for Energy-aware Hybrid Flow Shop Scheduling. In: Proceedings on the international conference on artificial intelligence (ICAI). 2014. The Steering committee of the world congress in computer science, computer engineering and applied computing (WorldComp) |
| [32] |
Zelenka, J (2010) Discrete event dynamic systems framework for analysis and modeling of real manufacturing system in Intelligent Engineering Systems (INES). In: 14th International conference on. 2010. IEEE |
| [33] |
Hubscher-Younger T, Mosterman PJ, DeLand S, Orqueda O, Eastman D (2012) Integrating discrete-event and time-based models with optimization for resource allocation. In: Simulation Conference (WSC), Proceedings of the 2012 Winter, pp 1–15 |
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
Sadre R, Haverkort B (2011) Decomposition-based queueing network analysis with FiFiQueues. In: Boucherie RJ, van Dijk NM (eds) Queueing networks, No. 154, Springer US, pp 643–699 |
| [38] |
Sadre R (2007) Decomposition-based analysis of queueing networks. University of Twente, Twente |
| [39] |
Zhu J, Hu L, Jiang Y, Khattak A (2017) Circulation network design for urban rail transit station using a PH(n)/PH(n)/C/C queuing network model[J]. Eur J Oper Res 260(3):1043–1068 |
/
| 〈 |
|
〉 |