Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

doi:10.1016/j.compenvurbsys.2018.05.009

	Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity
	Cheng, Shifen 1,2,3; Lu, Feng 1,2,3,4; Peng, Peng 1,2; Wu, Sheng 3,5
刊名	COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
	2018-09-01
卷号	71 页码:186-198
关键词	Short-term traffic forecasting Adaptive spatiotemporal k-nearest neighbor model Spatial heterogeneity Traffic patterns
ISSN号	0198-9715
DOI	10.1016/j.compenvurbsys.2018.05.009
通讯作者	Lu, Feng(luf@lreis.ac.cn)
英文摘要	Accurate and robust short-term traffic forecasting is a critical issue in intelligent transportation systems and realtime traffic-related applications. Existing short-term traffic forecasting approaches adopt fixed model structures and assume traffic correlations between adjacent road segments within assigned time periods. Due to the inherent spatial heterogeneity of city traffic, it is difficult for these approaches to obtain stable and satisfying results. To overcome the problems of fixed model structures and quantitatively unclear spatiotemporal dependency relationships, this paper proposes an adaptive spatiotemporal k-nearest neighbor model (adaptive-STKNN) for short-term traffic forecasting. It comprehensively considers the spatial heterogeneity of city traffic based on adaptive spatial neighbors, time windows, spatiotemporal weights and other parameters. First, for each road segment, we determine the sizes of spatial neighbors and the lengths of time windows for traffic influence using cross-correlation and autocorrelation functions, respectively. Second, adaptive spatiotemporal weights are introduced into the distance functions to optimize the candidate neighbor search mechanism. Next, we establish adaptive spatiotemporal parameters to reflect continuous changes in traffic conditions, including the number of candidate neighbors and the weight allocation parameter in the predictive function. Finally, we evaluate the adaptive-STKNN model using two vehicular speed datasets collected on expressways in California, U.S.A., and on city roads in Beijing, China. Four traditional prediction models are compared with the adaptive-STKNN model in terms of forecasting accuracy and generalization ability. The results demonstrate that the adaptive-STKNN model outperforms those models during all time periods and especially the peak period. In addition, the results also show the generalization ability of the adaptive-STKNN model.
资助项目	Key Research Program of the Chinese Academy of Sciences[ZDRW-ZS-2016-6-3] ; State Key Research Development Program of China[2016YFB0502104]
WOS关键词	FLOW PREDICTION ; REGRESSION ; ALGORITHM ; NETWORKS ; TIME
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology ; Geography ; Operations Research & Management Science
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000445169400016
资助机构	Key Research Program of the Chinese Academy of Sciences ; State Key Research Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/52917]
专题	中国科学院地理科学与资源研究所
通讯作者	Lu, Feng
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 5.Fuzhou Univ, Spatial Informat Res Ctr Fujian Prov, Fuzhou 350002, Fujian, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Shifen,Lu, Feng,Peng, Peng,et al. Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity[J]. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS,2018,71:186-198.
APA	Cheng, Shifen,Lu, Feng,Peng, Peng,&Wu, Sheng.(2018).Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity.COMPUTERS ENVIRONMENT AND URBAN SYSTEMS,71,186-198.
MLA	Cheng, Shifen,et al."Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity".COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 71(2018):186-198.