基于KNN回归的客运枢纽聚集人数组合预测方法

doi:10.13229/j.cnki.jdxbgxb20200288

Abstract

Abstract:

To work out a reasonable and scientific passenger-flow organization scheme with an accurate assembling passenger prediction for transportation terminals, a combination forecasting model based on the KNN regression algorithm was proposed. Grounded on the analysis of the assembling laws of passengers at transportation terminals, the KNN regression algorithm was applied to forecast the number of assembling passengers based on the principles of the numerical similarity and the trend similarity. With the comprehensive consideration of the respective characteristics, the combination forecasting was realized by introducing a time-varying weight coefficient. As a result, the proposed model could solve the shortcomings of the previous KNN regression prediction model, such as large amount of historical data and long running time. The experimental results suggest that the average prediction accuracy of the proposed method can be guaranteed over 95% during non-holidays and 90% during the Spring Festival travel rush, which is superior to moving average method, Kalman filter model and gray prediction method respectively.

Key words: engineering of transportation system, transportation terminal, k-nearest neighbor regression, combination forecasting, state vector, time-varying weight

CLC Number:

U491

Kai LU,Wei WU,Guan-rong LIN,Xin TIAN,Jian-min XU. Combination forecasting model for number of assembling passengers at transportation terminal based on KNN regression algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1241-1250.

Figures/Tables 13

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日期	11/7	11/8	11/9	11/10	11/11	11/12	11/13	均值
灰色预测法	7.80	5.33	3.24	7.15	5.53	4.57	4.48	5.44
卡尔曼滤波法	11.24	5.99	3.12	7.08	6.32	4.28	5.02	6.15
移动平均法	8.15	3.25	3.40	6.83	6.30	6.72	4.67	5.62
KNN组合	6.45	5.25	2.74	6.32	5.19	2.94	4.88	4.83

日期	灰色预测法	卡尔曼滤波法	移动平均法	KNN组合法	日期	灰色预测法	卡尔曼滤波法	移动平均法	KNN组合法
2/1	23.68	22.84	33.55	7.08	2/21	40.43	31.01	26.79	19.52
2/2	15.51	13.94	29.51	4.77	2/22	32.89	23.67	8.95	15.83
2/3	11.01	9.77	25.21	7.53	2/23	20.35	20.68	12.61	13.47
2/4	7.10	5.93	21.38	3.97	2/24	19.60	12.34	7.50	16.99
2/5	7.23	5.50	19.34	3.95	2/25	16.22	8.31	8.80	13.38
2/6	7.49	4.54	15.81	4.52	2/26	12.16	6.55	8.49	8.37
2/7	7.64	4.31	13.92	3.65	2/27	8.84	8.66	11.28	6.07
2/8	10.09	4.85	9.61	7.21	2/28	12.66	12.14	12.38	4.99
2/9	11.53	5.24	7.81	5.86	3/1	15.24	11.77	13.47	8.04
2/10	11.77	4.70	7.99	4.60	3/2	9.16	4.60	6.06	9.73
2/11	11.92	2.61	7.37	2.55	3/3	19.32	8.52	13.50	6.95
2/12	25.21	11.37	7.01	7.89	3/4	17.27	5.96	14.94	6.14
2/13	46.45	28.21	20.58	13.73	3/5	22.32	5.47	18.47	4.35
2/14	89.53	62.52	54.38	7.57	3/6	20.49	5.79	8.04	8.17
2/15	313.18	242.01	117.98	33.20	3/7	19.94	7.18	8.62	6.06
2/16	255.59	174.60	8.67	17.35	3/8	16.67	12.14	6.05	16.05
2/17	150.61	74.55	64.09	6.02	3/9	24.07	6.23	6.62	5.42
2/18	121.19	35.85	27.56	8.02	3/10	24.41	6.33	7.32	7.38
2/19	92.79	10.23	29.26	10.02	3/11	28.86	5.21	14.41	6.16
2/20	69.75	15.79	31.28	14.24	3/12	30.90	5.42	17.49	8.73

Combination forecasting model for number of assembling passengers at transportation terminal based on KNN regression algorithm

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