中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

文献类型：期刊论文

作者Cheng, Shifen1,2,3; Lu, Feng1,2,3,4; Peng, Peng1,2; Wu, Sheng3,5
刊名COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
出版日期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
DOI10.1016/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.
WOS关键词FLOW PREDICTION ; REGRESSION ; ALGORITHM ; NETWORKS ; TIME
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研究方向Computer Science ; Engineering ; Environmental Sciences & Ecology ; Geography ; Operations Research & Management Science
语种英语
WOS记录号WOS:000445169400016
出版者ELSEVIER SCI LTD
资助机构Key Research Program of the Chinese Academy of Sciences ; State Key Research Development Program of China
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.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。