TY - JOUR
T1 - Minimizing Metro Transfer Waiting Time with AFCS Data Using Simulated Annealing with Parallel Computing
AU - Liu, Xiaobo
AU - Huang, Minghua
AU - Qu, Hezhou
AU - Chien, Steven
N1 - Funding Information:
71671147 and supported by the Sichuan Development Program No. 2017GZ0371.
Funding Information:
This research was financially supported by the National Natural Science Foundation of China under grant No.
Publisher Copyright:
© 2018 Xiaobo Liu et al.
PY - 2018
Y1 - 2018
N2 - Coordinating train arrivals at transfer stations by altering their departure times can reduce transfer waiting time (TWT) and improve level of service. This paper develops a method to optimize train departure times from terminals that minimizes total TWT for an urban rail network with many transfer stations. To maintain service capacity and avoid operational complexity, dispatching headway is fixed. An integrated Simulated Annealing with parallel computing approach is applied to perform the optimization. To demonstrate model applicability and performance, the Shenzhen metro network is applied, where passenger flows (i.e., entry, transfer, and exit) at stations are approximated with the automatic fare collection system (AFCS) data. Results show that the total TWT can be significantly reduced.
AB - Coordinating train arrivals at transfer stations by altering their departure times can reduce transfer waiting time (TWT) and improve level of service. This paper develops a method to optimize train departure times from terminals that minimizes total TWT for an urban rail network with many transfer stations. To maintain service capacity and avoid operational complexity, dispatching headway is fixed. An integrated Simulated Annealing with parallel computing approach is applied to perform the optimization. To demonstrate model applicability and performance, the Shenzhen metro network is applied, where passenger flows (i.e., entry, transfer, and exit) at stations are approximated with the automatic fare collection system (AFCS) data. Results show that the total TWT can be significantly reduced.
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U2 - 10.1155/2018/4218625
DO - 10.1155/2018/4218625
M3 - Article
AN - SCOPUS:85055318601
SN - 0197-6729
VL - 2018
JO - Journal of Advanced Transportation
JF - Journal of Advanced Transportation
M1 - 4218625
ER -