The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo apresenta um modelo de rede de tráfego multicamadas e um método de agrupamento de redes de tráfego para resolver o problema de seleção de rota (RSP) no sistema de navegação automotiva (CNS). O objetivo do método proposto é reduzir substancialmente o tempo de cálculo da seleção de rota com perda aceitável de precisão, pré-processando a rede de tráfego de grande porte em uma nova forma de rede. A abordagem proposta pré-processa ainda mais a rede de tráfego do que o método tradicional de rede hierárquica pelo método de clustering. O agrupamento da rede de tráfego considera dois critérios. Especificamos um algoritmo de agrupamento genético para agrupamento de redes de tráfego e usamos NSGA-II para calcular o conjunto ideal de Pareto de múltiplos objetivos. O método proposto pode superar as limitações de tamanho ao resolver a seleção de rotas no CNS. As soluções fornecidas pelo algoritmo proposto são comparadas com as soluções ótimas para analisar e quantificar a perda de precisão.
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Feng WEN, Mitsuo GEN, Xinjie YU, "Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 8, pp. 2107-2115, August 2009, doi: 10.1587/transfun.E92.A.2107.
Abstract: This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2107/_p
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@ARTICLE{e92-a_8_2107,
author={Feng WEN, Mitsuo GEN, Xinjie YU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm},
year={2009},
volume={E92-A},
number={8},
pages={2107-2115},
abstract={This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.},
keywords={},
doi={10.1587/transfun.E92.A.2107},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2107
EP - 2115
AU - Feng WEN
AU - Mitsuo GEN
AU - Xinjie YU
PY - 2009
DO - 10.1587/transfun.E92.A.2107
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2009
AB - This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.
ER -