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Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach

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Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach. / Szeto, W. Y.; Jiang, Y.
In: Transportation Research Part B: Methodological, Vol. 67, 09.2014, p. 235-263.

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Szeto WY, Jiang Y. Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach. Transportation Research Part B: Methodological. 2014 Sept;67:235-263. Epub 2014 Jun 13. doi: 10.1016/j.trb.2014.05.008

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Szeto, W. Y. ; Jiang, Y. / Transit route and frequency design : Bi-level modeling and hybrid artificial bee colony algorithm approach. In: Transportation Research Part B: Methodological. 2014 ; Vol. 67. pp. 235-263.

Bibtex

@article{1619e8fadd6b457b8171ff9fd6c87a88,
title = "Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach",
abstract = "This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem. (C) 2014 Elsevier Ltd. All rights reserved.",
keywords = "Transit route and frequency setting problem, Bus network design, Bi-level programming, Artificial bee colony algorithm, Mixed integer program, Matheuristics, STRICT CAPACITY CONSTRAINTS, STOCHASTIC USER EQUILIBRIUM, NETWORK DESIGN, GENETIC ALGORITHM, ASSIGNMENT MODEL, MARKET REGIMES, FARE STRUCTURE, MASS-TRANSIT, TIME, SYSTEMS",
author = "Szeto, {W. Y.} and Y. Jiang",
year = "2014",
month = sep,
doi = "10.1016/j.trb.2014.05.008",
language = "English",
volume = "67",
pages = "235--263",
journal = "Transportation Research Part B: Methodological",
issn = "0191-2615",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",

}

RIS

TY - JOUR

T1 - Transit route and frequency design

T2 - Bi-level modeling and hybrid artificial bee colony algorithm approach

AU - Szeto, W. Y.

AU - Jiang, Y.

PY - 2014/9

Y1 - 2014/9

N2 - This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem. (C) 2014 Elsevier Ltd. All rights reserved.

AB - This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem. (C) 2014 Elsevier Ltd. All rights reserved.

KW - Transit route and frequency setting problem

KW - Bus network design

KW - Bi-level programming

KW - Artificial bee colony algorithm

KW - Mixed integer program

KW - Matheuristics

KW - STRICT CAPACITY CONSTRAINTS

KW - STOCHASTIC USER EQUILIBRIUM

KW - NETWORK DESIGN

KW - GENETIC ALGORITHM

KW - ASSIGNMENT MODEL

KW - MARKET REGIMES

KW - FARE STRUCTURE

KW - MASS-TRANSIT

KW - TIME

KW - SYSTEMS

U2 - 10.1016/j.trb.2014.05.008

DO - 10.1016/j.trb.2014.05.008

M3 - Journal article

VL - 67

SP - 235

EP - 263

JO - Transportation Research Part B: Methodological

JF - Transportation Research Part B: Methodological

SN - 0191-2615

ER -