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Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes

文献类型：期刊论文

作者Liu, Kang2,3,4; Gao, Song5; Qiu, Peiyuan2; Liu, Xiliang2; Yan, Bo4; Lu, Feng1,2,6
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2017-11-01
卷号6期号:11页码:14
关键词traffic interaction Word2Vec travel routes floating car data traffic forecasting
ISSN号2220-9964
DOI10.3390/ijgi6110321
通讯作者Lu, Feng(luf@lreis.ac.cn)
英文摘要Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods neglect the spatial heterogeneity of roads' traffic interactions, while the correlation-based methods cannot capture the non-linear dependency between two roads' traffic variables. In this paper, we propose a novel approach called Road2Vec to quantify the implicit traffic interactions among roads based on large-scale taxi operating route data using a Word2Vec model from the natural language processing (NLP) field. First, the analogy between transportation elements (i.e., road segment, travel route) and NLP terms (i.e., word, document) is established. Second, the real-valued vectors for road segments are trained from massive travel routes using the Word2Vec model. Third, the traffic interaction between any pair of roads is measured by the cosine similarity of their vectors. A case study on short-term traffic forecasting is conducted with artificial neural network (ANN) and support vector machine (SVM) algorithms to validate the advantages of the presented method. The results show that the forecasting achieves a higher accuracy with the support of the Road2Vec method than with the topological distance and traffic correlation based methods. We argue that the Road2Vec method can be effectively utilized for quantifying complex traffic interactions among roads and capturing underlying heterogeneous and non-linear properties.
WOS关键词FLOW ; MODEL
资助项目National Natural Science Foundation of China[41631177] ; Chinese Academy of Sciences[ZDRW-ZS-2016-6-3] ; National Key Research and Development Program[2016YFB0502104] ; University of Wisconsin-Madison, Office of the Vice Chancellor for Research and Graduate Education ; Wisconsin Alumni Research Foundation ; UCAS Joint PhD Training Program
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000416779300002
出版者MDPI AG
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences ; National Key Research and Development Program ; University of Wisconsin-Madison, Office of the Vice Chancellor for Research and Graduate Education ; Wisconsin Alumni Research Foundation ; UCAS Joint PhD Training Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/60988]  
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.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
6.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Kang,Gao, Song,Qiu, Peiyuan,et al. Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(11):14.
APA Liu, Kang,Gao, Song,Qiu, Peiyuan,Liu, Xiliang,Yan, Bo,&Lu, Feng.(2017).Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(11),14.
MLA Liu, Kang,et al."Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.11(2017):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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