Short-term Traffic Flow Prediction Algorithm Based on Combined Model

RUI Lanlan; LI Qinming

doi:10.11999/JEIT150846

Volume 38 Issue 5

May 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(5): 1227-1233

RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846

Citation:

RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846

RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846

Citation:

RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846

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Short-term Traffic Flow Prediction Algorithm Based on Combined Model

doi: 10.11999/JEIT150846

RUI Lanlan¹,
LI Qinming¹

Funds:

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)

Received Date: 2015-07-14
Rev Recd Date: 2016-01-08
Publish Date: 2016-05-19

Abstract

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 algorithms coefficient of detemination (R2 ) is close to 1, so the credibility of the model is higher than others.
- Short-term traffic flow,
- K-means algorithm,
- Extreme Learning Machine (ELM),
- Traffic Flow Prediction Based on Combined Model (TFPBCM)

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References(31)

References

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