基于组合模型的短时交通流量预测算法

doi:10.11999/JEIT150846

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Abstract

Traffic flow prediction is a key problem of realizing intelligent transportation technology. Forecasting traffic flow in time and accurately is the precondition to realize the dynamic traffic management. Short -term traffic flow prediction is an important part of traffic flow prediction. In this paper, the Traffic Flow Prediction Based on Combined Model (TFPBCM) based on traffic flow sequence partition and Extreme Learning Machine (ELM) is designed for the short time traffic flow forecasting. The algorithm divides the traffic flow into different patterns along a time dimension by K-means, and then models and forecasts for each pattern by ELM. The proposed algorithm is compared with Back Propagation (BP) and ELM. The combined model algorithm on modeling time is 1/10 of BP, but is 4 times ELM. Its MSE is 1/50 of BP and 1/20 of ELM. The combined model algorithm’s coefficient of detemination (R2 ) is close to 1, so the credibility of the model is higher than others.

Key words： Short-term traffic flow K-means algorithm Extreme Learning Machine (ELM) Traffic Flow Prediction Based on Combined Model (TFPBCM)

Received: 14 July 2015 Published: 11 March 2016

PACS:

TP393.07

Fund:

Funds for Creative Research Groups of China (61121061), The National Natural Science Foundation of China (61302078, 61372108), Beijing Higher Education Young Elite Teacher Project (YETP0476)

Corresponding Authors: LI Qinming E-mail: liqinming@bupt.edu.cn

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Cite this article:

RUI Lanlan,LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. JEIT, 2016, 38(5): 1227-1233.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150846 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I5/1227

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