Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II

Han, Zhenyu; Han, Baoming; Li, Dewei; Ning, Shangbin; Yang, Runhua; Yin, Yuehong

doi:10.1016/j.trb.2021.10.002

Cited by 38 publications

(14 citation statements)

References 31 publications

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“…Notably, the time periods of all stations on line l are normalized. e normalization of time periods reduces the dimension of variables effectively [22].…”

Section: Problem Statementmentioning

confidence: 99%

“…We describe dynamic passenger demand as smallgranularity cumulative number of arriving passengers p l it (at station i l at time t), alighting ratio of loaded passengers a l it (at station i l at time t) and transferring ratio of alighting passengers o ll′ jj′t (to transfer corridor (j l , j l′ ′ ) at time t). e data are processed from small-granularity origin-destination matrices for simplicity [22].…”

Section: Problem Statementmentioning

confidence: 99%

“…In population initialization, AANSGA-II adopts boundary individuals to generate the initial population reasonably. [22].…”

Section: Nondominated Sorting Coevolutionary Memetic Algorithmmentioning

confidence: 99%

“…We only focus on the improvements in NSCMA since AANSGA-II was introduced comprehensively in our previous work [22]. e improvements are reflected in chromosome construction, local search, and local sorting.…”

Section: Algorithm Improvementsmentioning

confidence: 99%

“…Wang et al [21] developed a demand-driven STOPO model to minimize passengers' waiting time and the number of passengers with failed transfers. Han et al [22] formulated a demand-driven STOPO model to minimize trains' operation cost and passengers' total time. AANSGA-II obtained an approximate Pareto Optimal Solution Set (POSS for short) efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Synchronous Optimization for Demand-Driven Train Operation Plan in Rail Transit Network Using Nondominated Sorting Coevolutionary Memetic Algorithm

Han

et al. 2022

Journal of Advanced Transportation

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In many cities and regions, decision makers independently develop Train Operation Plan (TOP) for each line in the rail transit network, resulting in a lack of TOP Synchronization (TOPS). Considering the entire network as a whole, researchers have realized that synchronous optimization is of great significance. In this paper, we formulate two Mixed-Integer Linear Programming (MILP) models to optimize demand-driven TOP in the network. The former is an Asynchronous TOP Optimization (ATOPO) model, while the latter is a Synchronous TOP Optimization (STOPO) model. The bi-objective models simultaneously determine train frequency, train timetable, and rolling stock circulation under small-granularity passenger demand to minimize trains’ total cost and passengers’ total time. Then, we propose the Nondominated Sorting Coevolutionary Memetic Algorithm (NSCMA) to solve the combinatorial optimization problems. The hybrid heuristic algorithm incorporates Coevolutionary Memetic Algorithm (CMA) into Advanced and Adaptive Nondominated Sorting Genetic Algorithm II (AANSGA-II) to ameliorate the evolution process for elite individuals. On this basis, we study the case of Shenyang Metro to verify the models and the algorithm. The results demonstrate that the STOPO model is better than the ATOPO model in reducing trains’ total cost and passengers’ total time. In addition, NSCMA is better than AANSGA-II in obtaining elite individuals.

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“…Notably, the time periods of all stations on line l are normalized. e normalization of time periods reduces the dimension of variables effectively [22].…”

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

“…In population initialization, AANSGA-II adopts boundary individuals to generate the initial population reasonably. [22].…”

Section: Nondominated Sorting Coevolutionary Memetic Algorithmmentioning

confidence: 99%

Section: Algorithm Improvementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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