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Optimal Configuration of the Metro Rail Transit Station Service Facilities by Integrated Simulation-Optimization Method Using Passengers’ Flow Fluctuation

  • Research Article - Civil Engineering
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Abstract

This paper proposes an integrated discrete-event simulation (DES) using phase-type (PH) distribution and optimization method for the performance assessment, as well as optimal configuration of metro rail transit (MRT) station service facilities. The MRT facilities (including ticket facilities and elevator) are represented as a queuing network system with the passengers’ arrival flow and the service time based on the PH distribution, which considers the flow and service time fluctuation. The necessary performance measures include facility utilization, mean number of passengers and waiting time of passengers in the facilities are obtained by PH-based DES model of service facilities. The model is then coupled with a genetic algorithm that works simultaneously to give the optimal configuration of service facilities. The results of PH-based DES model are first verified by existing PH-based analytical models and then compared with other existing exponential-based and deterministic-based queuing models. The results reveal that increase in squared coefficient of variation of arrival interval causes an increase in the performance measure values and high number of ticket facilities compared to existing models. The existing models therefore underestimate the results. The squared coefficient of variation is an important parameter and cannot be ignored in analysis and design of service facilities. This research provides a more realistic and novel PH-based DES, as well as PH-based simulation-optimization method that will assist the planners and designers of MRT stations to make intelligent decisions regarding analysis and design.

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Abbreviations

i :

Number of facility

\(\lambda _{a,i} \) :

Passengers’ arrival rate to the ith facility

E[X]:

Passengers’ arrival interval

\(\varepsilon \) :

Peak-hour factor

\(c_{a,i}^2 \) :

Squared coefficient of variation of arrival interval of the ith facility

\(\mu _{s,i} \) :

Mean service rate of the ith facility

\(c_{s,i}^2 \) :

Squared coefficient of variation of service time of the ith facility

c :

Number of servers in each facility

q :

Peak-hour volume (ped/h)

1F:

Ground floor

− 1F:

Lower ground floor

\(\alpha \) :

Sub-stochastic vector for the passengers’ arrival process

D:

Sub-generator matrix for the passengers’ arrival process

\(\beta \) :

Sub-stochastic vector for service process of the service facility

H:

Sub-generator matrix for service process of the service facility

\(\rho \) :

Utilization of the facility

\(E{[{N}_{q}]}\) :

Mean number of passengers in the queue of the facility

\(E{[{W}_{q}]}\) :

Mean waiting time of passengers in the queue of the facility

n :

Number of passengers in the facility

f :

Degree of Erlang Distribution

U :

Uniformly distributed random number

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Acknowledgements

We would like to express our sincere acknowledgement to National Natural Science Foundation of China (Serial Nos. 51578465 and 71402149), Basic Research Project of Sichuan Province, the Chinese government for the funding of doctoral program at Southwest Jiaotong University and the colleagues of National United Engineering Laboratory of Integrated and Intelligent Transportation at Southwest Jiaotong University, Chengdu for their support and valuable advices.

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Authors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China

    Afaq Khattak & Jiang Yangsheng

  2. Department of Civil Engineering, International Islamic University, Islamabad, Pakistan

    Afaq Khattak & Malik Muneeb Abid

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  1. Afaq Khattak
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  2. Jiang Yangsheng
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  3. Malik Muneeb Abid
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Correspondence to Afaq Khattak.

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Khattak, A., Yangsheng, J. & Abid, M.M. Optimal Configuration of the Metro Rail Transit Station Service Facilities by Integrated Simulation-Optimization Method Using Passengers’ Flow Fluctuation. Arab J Sci Eng 43, 5499–5516 (2018). https://doi.org/10.1007/s13369-018-3194-2

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