基于时空权重相关性的交通流大数据预测方法

doi:10.13209/j.0479-8023.2017.040

摘要/Abstract

摘要：

将分布式增量大数据聚合方法与交通流数据清洗规则相结合, 可以为交通流预测分析提供更准确可靠的数据源。通过交通流在路网中的相关性分析, 使用多阶路口转弯率构建空间权重矩阵, 完成对STARIMA交通流预测模型的改进。实验结果表明, 该方法可以在工作效率及准确程度上满足交通流大数据预测的需求, 为交通诱导信息发布提供依据。

关键词: 交通流, 大数据, 分布式增量, 路网相关性, STARIMA

Abstract:

A distributed incremental aggregation method combined with traffic flow data cleansing rules is proposed, and it can provide more accurate and reliable data for traffic flow forecast analysis. Through the correlation analysis of traffic flow in road network, the authors used the multi-allocation of turning rate in the intersection to build the spatial weight matrix, and improved the STARIMA traffic flow forecasting model. The experiment result proves that this method can meet the needs of traffic flow big-data forecasting in the efficiency and accuracy, and provide the basis for the traffic routing information.

Key words: traffic flow, big-data, distributed incremental, road network correlation, STARIMA

李欣, 罗庆, 孟德友. 基于时空权重相关性的交通流大数据预测方法[J]. 北京大学学报自然科学版, 2017, 53(4): 775-782.

Xin LI, Qing LUO, Deyou MENG. Traffic Flow-Big Data Forecasting Method Based on Spatial-Temporal Weight Correlation[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(4): 775-782.

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链接本文: https://xbna.pku.edu.cn/CN/10.13209/j.0479-8023.2017.040

https://xbna.pku.edu.cn/CN/Y2017/V53/I4/775

图/表 9

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下游路段编号	对应上游路段编号
6	7, 10, 14
8	5, 10, 14
9	5, 7, 14
13	5, 7, 10

下游路段编号	对应上游路段编号
6	7, 10, 14
8	5, 10, 14
9	5, 7, 14
13	5, 7, 10

算法	t/s
算法	2万条数据	5万条数据	10万条数据	20万条数据	50万条数据	100万条数据
MPI	3.595	9.573	18.365	36.879	91.368	180.774
MapReduce	16.786	31.213	43.357	60.674	87.849	135.407

算法	t/s
算法	2万条数据	5万条数据	10万条数据	20万条数据	50万条数据	100万条数据
MPI	3.595	9.573	18.365	36.879	91.368	180.774
MapReduce	16.786	31.213	43.357	60.674	87.849	135.407

路段编号	Dynamic STARIMA	路网相关 STARIMA	路段编号	Dynamic STARIMA	路网相关 STARIMA	路段编号	Dynamic STARIMA	路网相关 STARIMA
1	8638.27	6643.68	22	3338.29	2839.48	43	3977.67	3058.83
2	7396.01	6032.39	23	6236.77	5064.66	44	4458.98	3847.45
3	6631.31	4983.57	24	7784.39	5338.54	45	8979.37	7432.82
4	8362.58	5986.44	25	2526.67	1438.33	46	8443.69	6234.73
5	2948.21	1863.49	26	3464.32	1974.34	47	2798.55	1846.49
6	4820.22	2799.35	27	8764.39	5890.43	48	3985.42	2275.45
7	9230.23	7390.32	28	6549.48	5438.93	49	7849.43	5893.37
8	7857.46	6074.45	29	12974.45	8865.29	50	6692.38	5624.78
9	15324.01	12984.61	30	13084.44	10474.63	51	6639.32	4542.43
10	11479.46	8753.05	31	5478.34	3740.35	52	5873.57	4147.57
11	3892.27	2775.73	32	4858.34	3275.39	53	3720.32	2475.54
12	4917.48	3295.48	33	7434.95	6578.36	54	3920.28	2143.27
13	3729.33	2903.57	34	7868.23	5920.49	55	2039.58	1343.47
14	3928.23	2638.57	35	6743.23	4839.33	56	3235.62	1634.57
15	4478.19	3902.29	36	7820.33	5923.67	57	4838.88	3822.44
16	6903.36	4632.84	37	8068.35	6488.38	58	5749.23	4727.28
17	10573.48	7296.23	38	7819.28	6367.35	59	6403.45	4884.74
18	9033.54	7018.83	39	3894.22	2057.75	60	6653.63	4954.54
19	4847.34	3892.33	40	4780.44	3628.65	61	7570.49	6343.45
20	3309.21	3087.88	41	7897.36	4929.38	62	8788.48	6932.43
21	5789.22	4274.49	42	8902.34	5563.37