Research on Short-Term Traffic Flow Forecasting Based on KNN and Discrete Event Simulation

被引:7
|
作者
Yu, Shaozheng [1 ]
Li, Yingqiu [1 ]
Sheng, Guojun [1 ]
Lv, Jiao [1 ]
机构
[1] Dalian Neusoft Univ Informat, Dalian 116023, Peoples R China
关键词
Short-term traffic flow forecasting; Nonparametric regression; K nearest neighbor; Discrete event simulation;
D O I
10.1007/978-3-030-35231-8_63
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid development of urban traffic, it is very important to achieve accurate short-term traffic flow forecasting. Firstly, with the problem of short-term traffic flow forecasting, the key features that affect the traffic flow are extracted and the KNN non-parametric regression method is used for forecasting. Secondly, in order to solve the problem of dynamic traffic flow assignment, we build a simulation model and achieved good results. Finally, we use the case of short-term flow forecasting in airport to carry out a data experiment. The experimental results show that the traffic flow of traffic nodes and routes can be forecasted completely by using KNN algorithm combined with discrete event simulation technology, and the results are more credible.
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页码:853 / 862
页数:10
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