基于RBF神经网络的城市快速路短时交通流预测研究

doi:10.3976/j.issn.1002-4026.2012.03.005

Abstract

Abstract:

We analyzed and compared the effects of gray model GM (1,1) and RBF neural network model on short-term traffic flow prediction to test their feasibility and applicability. Practical instances show that gray model is inapplicable to the short-term prediction of traffic flow, but RBF neural network model is applicable. Moreover, we can acquire higher prediction accuracy when the distribution density of the radial basis function is from 0.8 to 1.0.

Key words: traffic flow, prediction, gray model, RBF neural network

CLC Number:

U491.1⁺12

ZHENG Xuan-Chuan, HAN Bao-Ming, LI De-Wei. RBF neural network based urban expressway short-term traffic flow prediction[J].J4, 2012, 25(3): 23-28.

References

Metrics

Comments

Recommended 0

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits third parties to freely share (i.e., copy and redistribute the material in any medium or format) and adapt (i.e., remix, transform, or build upon the material) the articles published in this journal, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated. The material may not be used for commercial purposes. For details of the CC BY-NC 4.0 license, please visit: https://creativecommons.org/licenses/by-nc/4.0

[1]	ZHANG Xudong, DUAN Chuanjun, WU Zhongjie, LI Si, WANG Chengyuan, LUO Ming. “Startup-grid connection” duration prediction method based on the response state of thermal power units [J]. Shandong Science, 2025, 38(4): 130-138.
[2]	WANG Jing, JIANG Mingyue, ZHAO Lin. Analysis of the coupling coordination and obstacle factors between green low-carbon systems and digital economy in the nine provinces(autonomous regions)along the Yellow River [J]. Shandong Science, 2025, 38(2): 89-99.
[3]	WANG Hengkun, GU Jin, SONG Zhifan, WANG Jiangfeng. A precise highway toll prediction model based on iTransformer [J]. Shandong Science, 2025, 38(1): 120-128.
[4]	LIU Zhizhen, HUANG Lumeng, SUN Yapeng, ZHANG Ying, LIU Zhendong. Ship heave motion prediction method based on wavelet transform and improved time series model [J]. Shandong Science, 2024, 37(6): 1-11.
[5]	WANG Xiaoyang, WAN Chenguang, WANG Xiaofeng. Prediction of mechanical properties of asphalt mixtures based on optimized neural networks [J]. Shandong Science, 2024, 37(4): 56-64.
[6]	ZHANG Hanxiao, LIU Yuran, LIU Yuan, NIU Zichen. A multitask learning model for the prediction of short-term subway passenger flow [J]. Shandong Science, 2024, 37(1): 95-106.
[7]	ZHANG Guojian, MENG Hao, XIONG Wei, BAI Tao, MENG Xianchen, WANG Jun, LÜ Xiao. Predicting surface movement and deformation for continuous mining and continuous backfilling under an artificial lake [J]. Shandong Science, 2023, 36(5): 33-43.
[8]	JIANG Jiawei, ZHAO Jinbao, LIU Wenjing, XU Yuejuan, LI Mingxing. Short-term prediction of urban railtransit passenger flow based on the Sparrow Search Algorithm-Long Short Term Memory combination model [J]. Shandong Science, 2023, 36(5): 75-84.
[9]	JIA Xingbin, GONG Xiang. Predicting interannual variation of global solar radiation trends in Jinan City based on time series sparse coefficient model [J]. Shandong Science, 2023, 36(1): 115-123.
[10]	SUN Feng-juan,TIAN Yong,SUN Kai-zheng,FU Hua-xuan,ZHANG Wen-juan,LI Min,L&xDC; Chen. Ozone prediction in Jinan based on artificial neural network ensemble prediction [J]. Shandong Science, 2022, 35(3): 89-99.
[11]	SHAN Ya-nan,LI Shu-bin,LIN Zhao-feng. Analysis of traffic accident characteristics and safety evaluation of urban expressways [J]. Shandong Science, 2022, 35(2): 115-123.
[12]	ZHU Shi-guang, SI Bing-feng, CUI Hong-meng, XUE Jing-wen. Travel destination prediction method for urban rail transit passengers using spatiotemporal feature extraction [J]. Shandong Science, 2021, 34(4): 104-113.
[13]	HOU Nan, LIU Long, JIANG Ting, TIAN Li-nan, DING Ning. Bearing life prediction based on real-time monitoring [J]. Shandong Science, 2021, 34(3): 42-48.
[14]	JIAO Xin-ping, WANG Jiang-feng, CHEN Lei, GAO Zhi-jun, DONG Jia-kuan, HUANG Hai-tao, YE Jing-song. Short-term traffic flow prediction algorithm based on HALRTC theory [J]. Shandong Science, 2019, 32(6): 62-68.
[15]	WEN Yong-qi, XIE Dong-fan, WANG Xiang, ZHOU Hao. Lane-changing strategy based on inter-vehicle communication [J]. Shandong Science, 2019, 32(4): 46-55.

RBF neural network based urban expressway short-term traffic flow prediction

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0