Car-following model based on radial basis function neural network

Xue Mei Ren; Ying Ping Zhu; Wu Hong Wang; Hong Huang

Car-following model based on radial basis function neural network

Xue Mei Ren^*
, Ying Ping Zhu
, Wu Hong Wang
, Hong Huang

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

It is hard to establish a precise car-following model because of the uncertainty in driver's behavior. A car-following model is developed based on the radial basis function (RBF) network. With this the nearest neighbor-clustering algorithm (NNCA) is improved, and the results of modeling are examined by the car-following data. The simulation results show that the proposed RBF network has a higher precision and requires shorter training in the prediction of the car-following model compared with the multilayer neural network.

Original language	English
Pages (from-to)	331-334
Number of pages	4
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	24
Issue number	4
Publication status	Published - Apr 2004

Keywords

Artificial neural network
Car-following
Nearest neighbor-clustering algorithm
Radial basis function network

Cite this

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title = "Car-following model based on radial basis function neural network",

abstract = "It is hard to establish a precise car-following model because of the uncertainty in driver's behavior. A car-following model is developed based on the radial basis function (RBF) network. With this the nearest neighbor-clustering algorithm (NNCA) is improved, and the results of modeling are examined by the car-following data. The simulation results show that the proposed RBF network has a higher precision and requires shorter training in the prediction of the car-following model compared with the multilayer neural network.",

keywords = "Artificial neural network, Car-following, Nearest neighbor-clustering algorithm, Radial basis function network",

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T1 - Car-following model based on radial basis function neural network

AU - Ren, Xue Mei

AU - Zhu, Ying Ping

AU - Wang, Wu Hong

AU - Huang, Hong

PY - 2004/4

Y1 - 2004/4

N2 - It is hard to establish a precise car-following model because of the uncertainty in driver's behavior. A car-following model is developed based on the radial basis function (RBF) network. With this the nearest neighbor-clustering algorithm (NNCA) is improved, and the results of modeling are examined by the car-following data. The simulation results show that the proposed RBF network has a higher precision and requires shorter training in the prediction of the car-following model compared with the multilayer neural network.

AB - It is hard to establish a precise car-following model because of the uncertainty in driver's behavior. A car-following model is developed based on the radial basis function (RBF) network. With this the nearest neighbor-clustering algorithm (NNCA) is improved, and the results of modeling are examined by the car-following data. The simulation results show that the proposed RBF network has a higher precision and requires shorter training in the prediction of the car-following model compared with the multilayer neural network.

KW - Artificial neural network

KW - Car-following

KW - Nearest neighbor-clustering algorithm

KW - Radial basis function network

UR - https://www.scopus.com/pages/publications/3142528629

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AN - SCOPUS:3142528629

SN - 1001-0645

VL - 24

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JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

IS - 4

ER -

Car-following model based on radial basis function neural network

Abstract

Keywords

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