软集合的代数结构及其应用研究
摘要
美国控制论专家Zadeh于1965年提出的模糊集理论、波兰数学家Pawlak于1982年提出的粗糙集理论和俄国数学家Molodtsov于1999年提出的软集理论在处理不确定性问题时都显示出各自的优势与特色。这三种理论相辅相成,具有紧密的联系。目前,软集理论研究主要集中在三个方面：一是对其基本理论的进一步完善；二是将软集理论与其它研究领域融合；三是软集理论的应用研究。模糊集理论、粗糙集理论和软集理论作为主要的处理不确定性问题的三种数学工具,其融合研究是目前的研究热点之一
众所周知,城市交通拥堵是各国发展中普遍存在的问题。随着交通需求量的增加,城市道路拥堵现象越来越严重,所造成的损失是难以估量的。如何准确地预测出即将形成的交通拥堵状态,并采取及时有效的措施,是近年来城市交通拥堵问题研究的热点之一。本文在相关研究的基础上从软模糊粗糙集理论的角度探讨预测城市交通拥堵的问题。本文旨在系统研究软集理论与模糊集理论以及格论的融合问题,探讨相关模型的性质,以及特定运算之下构成的代数结构,并在此基础上研究软集理论在城市交通拥堵预测中的应用。
本文的主要研究成果如下：
(1)提出了软格(soft lattice)的概念,证明了软格对于软集运算∩ε,n,U和八具有封闭性；基于∈-软集(∈-soft sets)和q-软集(q-soft sets)的概念,提出了理想软格(idealistic soft lattices)和滤子软格(filteristic soft lattices)的概念并讨论了它们的基本性质；基于Maji等提出的模糊软集(fuzzy soft sets)的概念,提出了软模糊格(soft fuzzy lattice)和软模糊理想(soft fuzzy ideal)的概念,并探讨它们构成的格结构；提出了生成模糊子格(generated fuzzy sublattice)的概念,给出了求一个格的任意子集的生成模糊子格的方法,并由此诱导出模糊子格之间的运算∪和U,证明了一个格的所有模糊子格在运算U和∩下构成一个格,一个格上的所有软模糊子格在运算U和∩下构成一个格。
(2)将软集理论应用于RSL-代数,提出了软RSL-代数(soft RSL-algebra)的概念,讨论了软RSL-代数的基本性质,定义了软RSL-子代数之间的运算U,Uε,∩和nε,证明了软RSL-代数在同态映射下具有保序性。
(3)基于Aktas和Cagman提出的软群概念和Acar提出的软环(soft ring)概念,研究了软群和软环所构成的代数结构,基于交换群的子群之间的并运算∪,诱导出交换群上软群之间的运算∪,证明了交换群的子群和交换群上的软群分别在运算∪和∪下的封闭性；证明了交换群上的软群在运算∪和∩下构成一个分配格。
(4)将软集理论应用于粗糙集理论,建立了一种软粗糙集的模型并讨论了它的代数结构,证明了特定论域上的所有软粗糙集可以构成一个格。提出一个基于软集的软模糊粗糙集模型,给出了软模糊粗糙集之间的运算∪,∪ε,∩和∩ε；证明了特定论域上的所有软模糊粗糙集关于运算∪,∩ε,以及∪ε,∩下分别构成分配格。
(5)基于软模糊粗糙集理论,研究了一种预测城市交通拥堵的方法。
The fuzzy set theory introduced by American Cybernetics expert Zadeh,the rough set theory introduced by Polish mathematician Pawlak in1982and the soft set theory introduced by Russian mathematician Molodtsov have shown their own advantages and characteristic in dealing with uncertainty. The three kinds of theories complement each other and they are closely related.
Now the research on the soft set theory focuses on three major aspects:the first is perfecting the soft theory, the second is merging it with the other studied fields; the third is on its application. As three kinds of major mathematical tool dealing with uncertainty, the study merging the fuzzy set theory, the rough set theory and the soft set theory is one of the hot spots at present.
It is well known that the urban traffic congestion is a general problem in the development of every country in the world. With the increase of traffic demand, urban traffic congestion becomes more serious. The direct and indirect economic loss caused by traffic congestion is amazing. How to predict accurately the forthcoming congestion and to take measures timely is one of the hot spots in the study on urban traffic congestion.
The intent of this paper is studying on the problems merging with the soft set theory,the fuzzy set theory, the rough set theory and the lattice theory and discussing the properties of the related models and the algebraic structures under the related operators. Based on the results, this paper presents a method predicting urban traffic congestion.
The main results and innovations in this thesis are summarized as follows:
1. The notion of the soft lattice is presented and the properties that the soft latt∩ice is closed under the operators∩ε,∩,∪and∧are derived. By means of∈-soft sets and q-soft sets, some characterizations of idealistic soft lattices and filteristic soft lattices are investigated. Based on the notion of the soft fuzzy set presented by Maji et al., the notions of the soft fuzzy lattice and the soft fuzzy ideal on a lattice are presented and their algebraic structures are discussed. The notion of generated fuzzy sublattices of an arbitrary subset of a lattice is presented and the method getting the generated fuzzy sublattices of an arbitrary subset is presented. The operator U between the fuzzy sublattices is inducted. The results are derived that the set of all fuzzy sublattices of a lattice forms a lattice on the operators∪and∩and that the set of all soft fuzzy sublattices forms a lattice on the operators U and∩.
2. Applying the soft set theory to the RSL-algebra,the notion of the soft RSL-algebra is presented and some related properties are derived, and the operators between the soft RSL-subalgebras∪,∩,∪ε and∩ε are defined. The result is derived that the RSL-algebra is isotonic under a homomorphic mapping.
3. Based on the notions of the soft groups presented by Aktas and Cagman and the soft rings presented by Acar, their algebraic structures are discussed, respectively. Based on the operator∪between the subgroups of a commutative group, the operator U between the soft groups on a commutative group is inducted and the results are derived that the subgroups of a commutative group and the soft groups on a commutative group are closed under the operators U and U, respectively. The result is derived that the set of all soft groups over a commutative group on the operators∩and∪forms a lattice.
4. Applying the soft set theory to the the rough set theory, the model of the soft rough set is constructed and its algebraic structures are discussed. The result is derived that the set of soft rough sets over a given initial universe forms a lattice. A model of soft fuzzy rough set based on a soft set is constructed, and the operators∪,∪ε,∩and∩ε between the soft fuzzy rough sets are presented. The results are derived that the set of soft fuzzy rough sets over a given initial universe under the operators∩and∪ε,∩ε and∪, forms a distributive lattice, respectively.
5. Based on the soft fuzzy rough set theory, a method predicting on urban traffic congestion is discussed.
众所周知,城市交通拥堵是各国发展中普遍存在的问题。随着交通需求量的增加,城市道路拥堵现象越来越严重,所造成的损失是难以估量的。如何准确地预测出即将形成的交通拥堵状态,并采取及时有效的措施,是近年来城市交通拥堵问题研究的热点之一。本文在相关研究的基础上从软模糊粗糙集理论的角度探讨预测城市交通拥堵的问题。本文旨在系统研究软集理论与模糊集理论以及格论的融合问题,探讨相关模型的性质,以及特定运算之下构成的代数结构,并在此基础上研究软集理论在城市交通拥堵预测中的应用。
本文的主要研究成果如下：
(1)提出了软格(soft lattice)的概念,证明了软格对于软集运算∩ε,n,U和八具有封闭性；基于∈-软集(∈-soft sets)和q-软集(q-soft sets)的概念,提出了理想软格(idealistic soft lattices)和滤子软格(filteristic soft lattices)的概念并讨论了它们的基本性质；基于Maji等提出的模糊软集(fuzzy soft sets)的概念,提出了软模糊格(soft fuzzy lattice)和软模糊理想(soft fuzzy ideal)的概念,并探讨它们构成的格结构；提出了生成模糊子格(generated fuzzy sublattice)的概念,给出了求一个格的任意子集的生成模糊子格的方法,并由此诱导出模糊子格之间的运算∪和U,证明了一个格的所有模糊子格在运算U和∩下构成一个格,一个格上的所有软模糊子格在运算U和∩下构成一个格。
(2)将软集理论应用于RSL-代数,提出了软RSL-代数(soft RSL-algebra)的概念,讨论了软RSL-代数的基本性质,定义了软RSL-子代数之间的运算U,Uε,∩和nε,证明了软RSL-代数在同态映射下具有保序性。
(3)基于Aktas和Cagman提出的软群概念和Acar提出的软环(soft ring)概念,研究了软群和软环所构成的代数结构,基于交换群的子群之间的并运算∪,诱导出交换群上软群之间的运算∪,证明了交换群的子群和交换群上的软群分别在运算∪和∪下的封闭性；证明了交换群上的软群在运算∪和∩下构成一个分配格。
(4)将软集理论应用于粗糙集理论,建立了一种软粗糙集的模型并讨论了它的代数结构,证明了特定论域上的所有软粗糙集可以构成一个格。提出一个基于软集的软模糊粗糙集模型,给出了软模糊粗糙集之间的运算∪,∪ε,∩和∩ε；证明了特定论域上的所有软模糊粗糙集关于运算∪,∩ε,以及∪ε,∩下分别构成分配格。
(5)基于软模糊粗糙集理论,研究了一种预测城市交通拥堵的方法。
The fuzzy set theory introduced by American Cybernetics expert Zadeh,the rough set theory introduced by Polish mathematician Pawlak in1982and the soft set theory introduced by Russian mathematician Molodtsov have shown their own advantages and characteristic in dealing with uncertainty. The three kinds of theories complement each other and they are closely related.
Now the research on the soft set theory focuses on three major aspects:the first is perfecting the soft theory, the second is merging it with the other studied fields; the third is on its application. As three kinds of major mathematical tool dealing with uncertainty, the study merging the fuzzy set theory, the rough set theory and the soft set theory is one of the hot spots at present.
It is well known that the urban traffic congestion is a general problem in the development of every country in the world. With the increase of traffic demand, urban traffic congestion becomes more serious. The direct and indirect economic loss caused by traffic congestion is amazing. How to predict accurately the forthcoming congestion and to take measures timely is one of the hot spots in the study on urban traffic congestion.
The intent of this paper is studying on the problems merging with the soft set theory,the fuzzy set theory, the rough set theory and the lattice theory and discussing the properties of the related models and the algebraic structures under the related operators. Based on the results, this paper presents a method predicting urban traffic congestion.
The main results and innovations in this thesis are summarized as follows:
1. The notion of the soft lattice is presented and the properties that the soft latt∩ice is closed under the operators∩ε,∩,∪and∧are derived. By means of∈-soft sets and q-soft sets, some characterizations of idealistic soft lattices and filteristic soft lattices are investigated. Based on the notion of the soft fuzzy set presented by Maji et al., the notions of the soft fuzzy lattice and the soft fuzzy ideal on a lattice are presented and their algebraic structures are discussed. The notion of generated fuzzy sublattices of an arbitrary subset of a lattice is presented and the method getting the generated fuzzy sublattices of an arbitrary subset is presented. The operator U between the fuzzy sublattices is inducted. The results are derived that the set of all fuzzy sublattices of a lattice forms a lattice on the operators∪and∩and that the set of all soft fuzzy sublattices forms a lattice on the operators U and∩.
2. Applying the soft set theory to the RSL-algebra,the notion of the soft RSL-algebra is presented and some related properties are derived, and the operators between the soft RSL-subalgebras∪,∩,∪ε and∩ε are defined. The result is derived that the RSL-algebra is isotonic under a homomorphic mapping.
3. Based on the notions of the soft groups presented by Aktas and Cagman and the soft rings presented by Acar, their algebraic structures are discussed, respectively. Based on the operator∪between the subgroups of a commutative group, the operator U between the soft groups on a commutative group is inducted and the results are derived that the subgroups of a commutative group and the soft groups on a commutative group are closed under the operators U and U, respectively. The result is derived that the set of all soft groups over a commutative group on the operators∩and∪forms a lattice.
4. Applying the soft set theory to the the rough set theory, the model of the soft rough set is constructed and its algebraic structures are discussed. The result is derived that the set of soft rough sets over a given initial universe forms a lattice. A model of soft fuzzy rough set based on a soft set is constructed, and the operators∪,∪ε,∩and∩ε between the soft fuzzy rough sets are presented. The results are derived that the set of soft fuzzy rough sets over a given initial universe under the operators∩and∪ε,∩ε and∪, forms a distributive lattice, respectively.
5. Based on the soft fuzzy rough set theory, a method predicting on urban traffic congestion is discussed.
引文
[1]D.Molodtsov. Soft Set Theory-First Results. Computers and Mathematics with Applications.1999,37(4):19-31
[2]P.K.Maji, R.Biswas, A.R.Roy. Soft Set Theory. Computers and Mathematics with Applications.2003,45(4):555-562
[3]Z. Pawlak. Rough sets:Theoretical aspects of reasoning about data. Boston:Kluwer Academic Pulishers,1991
[4]Z. Pawlak. Rough sets. International Journal of Parallel Programming,1982,11(5):341-356
[5]Z. Pawlak. Rough sets and fuzzy sets. Fuzzy sets and Systems,1985,17(1):99-102
[6]Zadeh L.A..Fuzzy Sets.Information and Control,1965,8(3):338-353
[7]M.I.Ali, F.Feng, X.liu, W.K.Min, M.Shabir. On some new operations in soft set theory. Computers and Mathematics with Applications.2009,57(9):1547-1553
[8]K.Qin, Z.Hong. On soft equality. Journal of Computational and Applied Mathematics, 2010,234(5):1347-1355
[9]徐扬.格蕴涵代数.西南交通大学学报,1993,1(1)：20-27
[10]Y.Xu, K.Qin. On fuzzy filters of lattice implication algebras. Journal of Fuzzy Mathematics,1993,2(1):251-260
[11]秦克云,赵华.模糊粗糙群.模糊系统与数学,2011,25(4)：162-169
[12]GLiu.Generalized rough sets over fuzzy lattices. Information Sciences,2008,178(6): 1651-1662
[13]秦克云,裴峥,杜卫锋.模糊近似算子的拓扑性质.系统工程学报,2005.41(1)：81-85
[14]秦克云,乔全喜.模糊集的拓扑结构.计算机科学,2007,34(12)：161-162
[15]秦克云,徐扬.格值命题逻辑系统L(X).模糊系统与数学,1997,11(4)：5-11
[16]秦克云,涂文彪.粗糙集代数与格蕴涵代数.西南交通大学学报,2004,39(6)：754-757
[17]秦克云,吴正江.基于集值映射的近似算子.广西师范大学学报(自然科学版),2007,25(4)：48-50
[18]D.Meng, X. Zhang, K. Qin. Soft rough fuzzy sets and soft fuzzy rough sets. Computers and Mathematics with Applications,2011,62(12):4635-4645
[19]王珏,刘三阳,张杰.基于广义粗糙近似的信息检索方法研究[J].系统工程与电子技术,2004,26(12)：1421-1426
[20]罗威.基于向量空间的中文概念检索技术研究[J].情报与实践,2003,26(3)：226-229
[21]张小红.模糊逻辑及其代数分析.科学出版社,2008
[22]F.Feng, Y. Li, V.Leoreanu-Fotea. Application of level softs in decision making based on interval-valued fuzzy soft sets. Computers and Mathematics with Applications,2010,60(6): 1756-1767
[23]Dudios D., Prade H.. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems,1990,17(2-3):191-209
[24]彭祖赠,孙韫玉.模糊(Fuzzy)数学及其应用.武汉大学出版社,2002
[25]宋晓秋.模糊数学原理与方法.第二版.中国矿业大学出版社,2004
[26]H.Aktas, N.Cagman. Soft sets and soft groups. Science Direct. Information Sciences, 2007,177(13):2726-2735
[27]F.Feng, X.Liu,V.Leoreanu-Fotea, Y.B.Jun. Soft sets and soft rough sets.Information Sciences,2011,181(6):1125-1137
[28]A.A.Estaji, M.R.Hooshmandasl, B.Davvaz. Rough set theory applied to lattice theory. Information Sciences,2012,200:108-122
[29]J.Zhan,Y. B. Jun.Soft BL-algebras based on fuzzy sets.Computers and Mathematics with Applications,2010,59(6):2037-2046
[30]U.Acar, F.Koyuncu, B.Tanay. Soft sets and soft rings.Computers and Mathematics with Applications,2010,59(11):3458-3463
[31]张禾瑞.近世代数基础.高等教育出版社,2004
[32]聂灵沼,丁石孙.代数学引论.第二版.高等教育出版社,2000
[33]汤志康,王伟智,谈蔚欣.基于Bagging的交通拥堵预测研究.集美大学学报(自然科学版),2006,11(2)：156-160
[34]房丽侠,魏连雨,闫伟阳.基于决策树的交通拥挤状态预测.河北工业大学学报,2010,,39(2)：105-110
[35]N.Cagman, S.Enginoglu. Soft set theory and uni-int decision making. European Journal of Operational Research,2010,207(2):848-855
[36]阎瑞霞,刘金良,姚炳学,王翔.模糊软集与模糊软群.数学的实践与认识,2001,40(8)：144-148
[37]A.A.Estaji, S. Khodaii, S.Bahrami. On rough set and fuzzy sublattice. Information Sciences,2011,181(18):3981-3994
[38]B.Dawaz. Roughness based on fuzzy ideals. Information Sciences,2006,176(16): 2417-2437
[39]杨纶标,高英仪.模糊数学原理及应用.华南理工大学出版社,2005
[40]安秋生.粗糙关系数据库.电子工业出版社.2009
[41]吴丹,齐和庆.信息检索模型及其在跨语言信启、检索中的应用进展.现代情报.2009,29(7)：215-221
[42]付雪峰,刘邱云,王明文.基于互信息的粗糙集信息检索模型.山东大学学报,2006, 41(3)：116-119
[43]谭德坤.模糊粗糙集在科技文档检索中的应用研究.计算机仿真,2011,28(10)：168-172
[44]许金权.模糊粗糙集的表示及应用.模糊系统与数学,,2011,25(1)：146-152
[45]A.M.Radzikowska, E.E.Kerre. A comparative study of fuzzy rough sets. Fuzzy Sets and Systems,2002,126(2):137-155
[46]X.Ma, J.Zhan.On (∈,∈ ∨q)-fuzzy filters of BL-algebras. Journal of Systems Sciences and Complexity,2008,21(1):144-158
[47]S.K.Bhakat, P.Das. (∈,∈∨q)-fuzzy subgroups. Fuzzy Sets and Systems,1996,80(3): 359-368
[48]B.Dawaz. (∈,∈∨q)-fuzzy subnear-rings and ideals.Soft Computing,2006,10(3): 206-211
[49]GGratzer.General lattice theory. Academic Press.INC.1978
[50]G Birkhoff. Lattice Theory. Trans.Amer.Math.Soc. New York,1967
[51]张小红,祝峰.Rough逻辑系统RSL与模糊逻辑系统Luk.电子科技大学学报,2011,40：296-302
[52]Esteva F., Godo L.. Monoidal t-norm based logic:towards a logic for left-continuous t-norms. Fuzzy Sets and Systems,2001,124(3):271-288
[53]A.Aygunoglu, H. Aygun. Introduction to fuzzy soft groups. Computers and Mathematics with Applications,2009,58(6):1279-1286
[54]F.Feng, Y. B. Jun, X. Z. Zhao. Soft semirings. Computers and Mathematica with Applicationa,2008,56(10):2621-2628
[55]P.Majumdar, S.K.Samanta. Generalised fuzzy soft sets. Computers and Mathematics with Applications,2010,59(4):1425-1432
[56]Q. Hu, S. An, D. Yu. Soft fuzzy rough sets for robust feature evaluation and selection. Information Sciences,2010,180(22):4384-4400
[57]C.Wang, D.Chen. A short note on some properties of rough groups. Computers and Mathematics with Applications,2010,59(1):431-436
[58]A.Rosenfeld. Fuzzy groups. Journal of Mathematical Analysis and Applications,1971, 35:512-517
[59]N.Kuroki, P.Wang. The lower and upper approximations in a Fuzzy Group. Information Sciences,1996,90(1-4):203-220
[60]王国俊.论Fuzzy格的构造.数学学报,1986.29(2)：1083
[61]Q. Sun, Z.Zhang, J. Liu. Soft sets and soft modules. Rough Sets and Knowledge Technology. Lecture Notes in Computer Science,2008,5009:403-409
[62]Y. B. Jun. Soft BCK/BCI-algebras. Computers and Mathematics with Applications, 2008,56(5):1408-1413
[63]P. K. Maji, A. R. Roy, R. Biswas. An application of soft sets in a decision making problem. Computers and Mathematics with Applications,2002,44(8-9):1077-1083
[64]D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang. The parameterization reduction of soft sets and its applications. Computers and Mathematics with Applications,2005,49(5-6): 757-763
[65]I.DAuntsh, M. Winter. Rough Relation Algebras Revisited. Fundamenta Informaticae, 2006,74(2-3):283-300
[66]R.Biswas, S.Nanda. Rough groups and rough subgroups. Bulletin of the Polish Academy os Sciences Mathematics,1994,42(3):251-254
[67]M.I.Ali. A note on soft sets, rough soft sets and fuzzy soft sets. Applied Soft Computing, 2011,11(4):3329-3332
[68]Y. She, G. Wang. An axiomatic approach of fuzzy rough sets based on residuated lattices. Computers and Mathematics with Applications,2009,58(1):189-201
[69]N.Kuroki. Rough ideals in Semigroups. Information Sciences,1997,100(1):139-163
[70]B.Dawaz, M.Mahdavipour. Roughness in modules. Information Sciences,2006,176 (24):3658-3674
[71]Q.Xiao, Z.Zhang. Rough prime ideals and rough fuzzy prime ideals in semigroups. Information Sciences,2006,176(6):725-733
[72]J.Jiang, C.Wu, D.Chen. The product structure of fuzzy rough sets on a group and the rough T-fuzzy group. Information Sciences,2005,175(1):97-107
[73]Y. Xu, D. Ruan, K. Qin, J. Liu. Lattice-valued logic. Springer-Verlin Berlin Heidelberg New York,2003
[74]P.K.Maji, R.Biswas, A.R.Roy. Fuzzy soft sets. Journal of Fuzzy Mathematics,2001, 9(3):589-602
[75]P.K.Maji, R.Biswas, A.R.Roy. Intuitionistic fuzzy soft sets. Journal of Fuzzy Mathematics,2001,9(3):677-691
[76]X.Yang, T.Lin, J.Yang. Combination of interval-valued fuzzy set and soft set. Computers and Mathematics with Applications,2009,58(3):521-527
[77]W.Xu, J.Ma, S.Wang, G.Hao.Vague soft sets and their properties. Computers and Mathematics with Applications,2010,59(2):787-794
[78]G.Deschrijver, E.E.Kerre. On the relationship between some extensions of fuzzy set theory. Fuzzy Sets and Systems.2003,133(2):227-235
[79]张文修,姚一豫,梁怡.粗糙集与概念格[M].西安交通大学出版社,2006
[80]张文修,吴伟志.粗糙集理论介绍和研究综述.模糊系统与数学,2000,14(4)：1-12
[81]张文修,梁怡,吴伟志.信息系统与知识发现.科学出版社,2003
[82]王宗军,李红侠,邓晓岚.粗糙集理论研究的最新进展及其发展趋势.武汉理工大学学报,2006,28(1)：43-47
[83]胡可云,陆玉昌,石纯一.粗糙集理论及其应用进展.清华大学学报(自然科学版),2001,41(1)：64-68
[84]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.科学出版社,2001
[85]杨孝先,杨坚.信息论基础.中国科学技术出版社,2011
[86]Z.Kong, L.Gao, L.Wang. Comment on "A fuzzy soft set theoretic approach to decision Making problems". Journal of Computational and Applied Mathematics,2009,223(2): 540-542
[87]Y.Jiang, Y. Tang, Q. Chen. An adjustable approach to intuitionistis fuzzy soft sets based decision making. Applied Mathematical Modelling,2011,35(2):824-836
[88]Fu Li.Soft lattices.Global Journal of Science Frontier Research,2010,10(4):157-159
[89]Y.B.Jun, C.H.Park. Applications of soft sets in ideal theory of BCK/BCI-algebras. Information Sciences,2008,178(11):2466-2475
[90]曲炜,朱诗兵.信息论基础及应用.清华大学出版社,2005
[91]Q.Xiao, Z. Zhang. Rough prime ideals and rough fuzzy prime ideals in semigroups. Information Sciences,2006,176(6):725-733
[92]N.Kuroki. Rough ideals in semigroups. Information Sciences,1997,100(1-4):139-163
[93]O.Kazanc, B.Davvaz. On the structure of rough prime (primary) ideals and rough fuzzy prime (primary) ideals in commutative rings. Information Sciences,2008,178(5):1343-1354
[94]周荫清.信息论基础.第3版.北京航空航天大学出版社,2006
[95]B.Dawaz. A short note on algebraic T-rough sets. Information Sciences,2008,178(16): 3247-3252
[96]B.Dawaz. Roughness in rings. Information Sciences,2004,164(1-4):147-163
[97]B.A.Davey, H.A.Priestley. Introduction to Lattices and Order, seconded. Cambridge University Press,2002
[98]丁晶,王文圣,赵永龙.以互信息为基础的广义相关系数.四川大学学报(工程科学版),2002,34(3)：1-5
[99]X.Yuan, C.Zhang, Y.Ren.Generalized fuzzy groups and many valued applications. Fuzzy Sets Syst.,2003,138(1):205-211
[100]P.Majumdar, S.K.Samanta. On soft mapping. Computers and Mathematics with Applications,2010.60(9):2666-2672
[101]F. Feng, C.Li, B. Davvaz, M.I.Ali. Soft sets combined with fuzzy sets and rough sets:a tentative approach[J]. Soft Computing,2010,14(9):899-911
[102]陆钟万.面向计算机科学的数理逻辑.第二版.科学出版社,2001
[103]向大晶,刘先平.关于软集理论的综述报告.湖北民族学院学报(自然科学版)2009,27(4)：361-365
[104]李龙澍,张霞.基于Rough集的情报检索研究.情报学报,2002,21(1)：35-45
[105]王灿辉,张敏,马少平.自然语言在处理信息检索中的应用综述.中文信息学报.2007,21(2)：35-45
[106]何薇,徐伟华.信息检索的粗糙集方法.重庆理工大学学报(自然科学),2010,24(9)：84-88
[107]张莉.城市交通拥堵问题研究.决策,,2011,5：6-7
[108]刘晓.关于城市交通拥堵问题研究的文献综述.经济研究导刊,,2010,4：102-103
[109]张元亮,卢鹏.基于粗糙集的城市交通拥堵预警算法分析.交通科技与经济,2009,2：74-76
[110]刘智勇.智能交通控制理论及其应用[M].科学出版社,,2003
[111]王炜,过秀成.交通工程学[M].东南大学出版社,2000
[112]陆化普.城市交通现代化管理[M].人民交通出版社,,2002
[113]宁宣熙.堵塞流理论及其应用[M].科学出版社,2002
[114]席建锋,王晓燕,王双维,王艳玲,邓毅萍.基于粗糙集的道路交通事故成因层次分析方法.长春理工大学学报(自然科学版),2009,32(2)：257-259
[115]张鹏,张靖,刘玉增.粗糙集在交通事故黑点成因分析中的应用[J].电子科技大学学报,2007,36(2)：267-270
[116]姚智胜,邵春福,龙德璐.基于粗糙集理论的路段交通事故多发点成因分析[J].中国安全科学学报,2005,15(12)：107-109
[117]张一鸣,尹春桥,王鹏.基于模糊推理的交通拥堵判断方法.西部交通科技,2012,02：64-69
[118]李兰冰.我国城市交通拥挤的成因及其对策研究[J].理论学刊,2005,6：33-35
[119]冯金巧.城市道路交通拥挤预测关键技术研究.吉林大学博士毕业论文,2008
[120]美国智能交通信息发展及现状[R].北京交管局赴美智能交通管理培训组技术报告.2002：2-10
[121]王振强.城市交通拥堵问题及解决途径[J].甘肃科技,2002,18(10)：1-7
[122]张晓亮,张可,刘浩,李静,陈希.基于FCM-粗糙集的城市快速路交通状态判别.系统工程，2010,28(8)：74-80
[123]刘浩,张晓亮,张可.基于粗糙集交通信息提取计算的城市道路行程时间预测.公路交通科技,2008,25(10)：117-122
[124]吴宏娜,韩印.城市交通拥挤事件的模糊C均值聚类识别方法研究.价值工程,,2011,(07)：184-186
[125]白俊鸽,陈阳,赵良.基于数据挖掘的交通状态决策信息融合模型的研究.硅谷,2010,(15)：92
[126]马世勇,郑应文,柯栋忠.城市交通拥堵自动判别方法研究.福建电脑,2007,(1)：7-8
[127]李林波,万艳花,张勇平.城市交通拥挤发生的一般规律[J].山东交通学院学报,2004,12(1)：55-59
[128]谈晓洁,周晶,盛昭瀚.城市交通拥挤特征及疏导决策分析[J].管理工程学报,2003,17(1)：56-60
[129]姜桂艳.道路交通状态判别技术与应用[M].北京：人民交通出版社,2004
[130]戢晓峰,刘澜,吴其刚.区域路网交通信息提取方法.西南交通大学学报,2008,43：422-426
[131]刘静,关伟.交通流预测方法综述.公路交通科技,2004,21(3)：82-85
[132]陆化普,李瑞敏,朱茵.智能交通系统概论[M].北京：中国铁道出版社,2004
[133]贺国光,马寿峰,李宇.基于小波分解与重构的交通流短时预测法[J].系统工程理论与实践,2002,(9)：101-106
[134]杜豫川,周小鹏,郝媛,孙立军.基于实时检测数据的交通拥堵检测算法研究.第二届中国智能交通年会论文集,2006,313-316
[135]B.L.Brian, M.J.Demertsky. Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. Systems,Man and Cybernetics, 1994. Humans, Information and Technology,1994 IEEE International Conference on (2) 1706-1709
[136]张黎.交通流预测评价中如何运用模糊数学方法进行预测.科技向导,2010,(27)：185-186
[137]卫寒梅,郑应文.基于粗糙集和D-S证据理论的交通拥堵预警.福州大学学报(自然科学版),2008,,36(1)：69-72
[138]Z.Kong, L.Gao, L. Wang, S. Li, The normal paremeter reduction of soft sets and its algorithm.Computers and Mathematics with Applications.2008,56,3029-3037
[139]A.R.Roy, P. K. Maji, A fuzzy soft set theoretic approach to decision making problems. Journal of Computational and Applied Mathematics.2007,203:412-418
[140]Lei Fan, William W.Wilson, BruceDahl. Congestion, port expansion and spatial competition for US container imports. Transportation Research Part E,2012,48:1121-1136
[141]J.Wang, L.Wang. Congestion analysis of traffic networks with direction-dependant heterogeneity. Physica A,2013,392:392-399
[142]H. Li, Daniel J. Graham, Arnab Majumdar. The effects of congestion charging on road traffic casualties:A causal analysis using difference-in-difference estimation. Accident Analysis and Prevention,2012,49:366-377
[2]P.K.Maji, R.Biswas, A.R.Roy. Soft Set Theory. Computers and Mathematics with Applications.2003,45(4):555-562
[3]Z. Pawlak. Rough sets:Theoretical aspects of reasoning about data. Boston:Kluwer Academic Pulishers,1991
[4]Z. Pawlak. Rough sets. International Journal of Parallel Programming,1982,11(5):341-356
[5]Z. Pawlak. Rough sets and fuzzy sets. Fuzzy sets and Systems,1985,17(1):99-102
[6]Zadeh L.A..Fuzzy Sets.Information and Control,1965,8(3):338-353
[7]M.I.Ali, F.Feng, X.liu, W.K.Min, M.Shabir. On some new operations in soft set theory. Computers and Mathematics with Applications.2009,57(9):1547-1553
[8]K.Qin, Z.Hong. On soft equality. Journal of Computational and Applied Mathematics, 2010,234(5):1347-1355
[9]徐扬.格蕴涵代数.西南交通大学学报,1993,1(1)：20-27
[10]Y.Xu, K.Qin. On fuzzy filters of lattice implication algebras. Journal of Fuzzy Mathematics,1993,2(1):251-260
[11]秦克云,赵华.模糊粗糙群.模糊系统与数学,2011,25(4)：162-169
[12]GLiu.Generalized rough sets over fuzzy lattices. Information Sciences,2008,178(6): 1651-1662
[13]秦克云,裴峥,杜卫锋.模糊近似算子的拓扑性质.系统工程学报,2005.41(1)：81-85
[14]秦克云,乔全喜.模糊集的拓扑结构.计算机科学,2007,34(12)：161-162
[15]秦克云,徐扬.格值命题逻辑系统L(X).模糊系统与数学,1997,11(4)：5-11
[16]秦克云,涂文彪.粗糙集代数与格蕴涵代数.西南交通大学学报,2004,39(6)：754-757
[17]秦克云,吴正江.基于集值映射的近似算子.广西师范大学学报(自然科学版),2007,25(4)：48-50
[18]D.Meng, X. Zhang, K. Qin. Soft rough fuzzy sets and soft fuzzy rough sets. Computers and Mathematics with Applications,2011,62(12):4635-4645
[19]王珏,刘三阳,张杰.基于广义粗糙近似的信息检索方法研究[J].系统工程与电子技术,2004,26(12)：1421-1426
[20]罗威.基于向量空间的中文概念检索技术研究[J].情报与实践,2003,26(3)：226-229
[21]张小红.模糊逻辑及其代数分析.科学出版社,2008
[22]F.Feng, Y. Li, V.Leoreanu-Fotea. Application of level softs in decision making based on interval-valued fuzzy soft sets. Computers and Mathematics with Applications,2010,60(6): 1756-1767
[23]Dudios D., Prade H.. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems,1990,17(2-3):191-209
[24]彭祖赠,孙韫玉.模糊(Fuzzy)数学及其应用.武汉大学出版社,2002
[25]宋晓秋.模糊数学原理与方法.第二版.中国矿业大学出版社,2004
[26]H.Aktas, N.Cagman. Soft sets and soft groups. Science Direct. Information Sciences, 2007,177(13):2726-2735
[27]F.Feng, X.Liu,V.Leoreanu-Fotea, Y.B.Jun. Soft sets and soft rough sets.Information Sciences,2011,181(6):1125-1137
[28]A.A.Estaji, M.R.Hooshmandasl, B.Davvaz. Rough set theory applied to lattice theory. Information Sciences,2012,200:108-122
[29]J.Zhan,Y. B. Jun.Soft BL-algebras based on fuzzy sets.Computers and Mathematics with Applications,2010,59(6):2037-2046
[30]U.Acar, F.Koyuncu, B.Tanay. Soft sets and soft rings.Computers and Mathematics with Applications,2010,59(11):3458-3463
[31]张禾瑞.近世代数基础.高等教育出版社,2004
[32]聂灵沼,丁石孙.代数学引论.第二版.高等教育出版社,2000
[33]汤志康,王伟智,谈蔚欣.基于Bagging的交通拥堵预测研究.集美大学学报(自然科学版),2006,11(2)：156-160
[34]房丽侠,魏连雨,闫伟阳.基于决策树的交通拥挤状态预测.河北工业大学学报,2010,,39(2)：105-110
[35]N.Cagman, S.Enginoglu. Soft set theory and uni-int decision making. European Journal of Operational Research,2010,207(2):848-855
[36]阎瑞霞,刘金良,姚炳学,王翔.模糊软集与模糊软群.数学的实践与认识,2001,40(8)：144-148
[37]A.A.Estaji, S. Khodaii, S.Bahrami. On rough set and fuzzy sublattice. Information Sciences,2011,181(18):3981-3994
[38]B.Dawaz. Roughness based on fuzzy ideals. Information Sciences,2006,176(16): 2417-2437
[39]杨纶标,高英仪.模糊数学原理及应用.华南理工大学出版社,2005
[40]安秋生.粗糙关系数据库.电子工业出版社.2009
[41]吴丹,齐和庆.信息检索模型及其在跨语言信启、检索中的应用进展.现代情报.2009,29(7)：215-221
[42]付雪峰,刘邱云,王明文.基于互信息的粗糙集信息检索模型.山东大学学报,2006, 41(3)：116-119
[43]谭德坤.模糊粗糙集在科技文档检索中的应用研究.计算机仿真,2011,28(10)：168-172
[44]许金权.模糊粗糙集的表示及应用.模糊系统与数学,,2011,25(1)：146-152
[45]A.M.Radzikowska, E.E.Kerre. A comparative study of fuzzy rough sets. Fuzzy Sets and Systems,2002,126(2):137-155
[46]X.Ma, J.Zhan.On (∈,∈ ∨q)-fuzzy filters of BL-algebras. Journal of Systems Sciences and Complexity,2008,21(1):144-158
[47]S.K.Bhakat, P.Das. (∈,∈∨q)-fuzzy subgroups. Fuzzy Sets and Systems,1996,80(3): 359-368
[48]B.Dawaz. (∈,∈∨q)-fuzzy subnear-rings and ideals.Soft Computing,2006,10(3): 206-211
[49]GGratzer.General lattice theory. Academic Press.INC.1978
[50]G Birkhoff. Lattice Theory. Trans.Amer.Math.Soc. New York,1967
[51]张小红,祝峰.Rough逻辑系统RSL与模糊逻辑系统Luk.电子科技大学学报,2011,40：296-302
[52]Esteva F., Godo L.. Monoidal t-norm based logic:towards a logic for left-continuous t-norms. Fuzzy Sets and Systems,2001,124(3):271-288
[53]A.Aygunoglu, H. Aygun. Introduction to fuzzy soft groups. Computers and Mathematics with Applications,2009,58(6):1279-1286
[54]F.Feng, Y. B. Jun, X. Z. Zhao. Soft semirings. Computers and Mathematica with Applicationa,2008,56(10):2621-2628
[55]P.Majumdar, S.K.Samanta. Generalised fuzzy soft sets. Computers and Mathematics with Applications,2010,59(4):1425-1432
[56]Q. Hu, S. An, D. Yu. Soft fuzzy rough sets for robust feature evaluation and selection. Information Sciences,2010,180(22):4384-4400
[57]C.Wang, D.Chen. A short note on some properties of rough groups. Computers and Mathematics with Applications,2010,59(1):431-436
[58]A.Rosenfeld. Fuzzy groups. Journal of Mathematical Analysis and Applications,1971, 35:512-517
[59]N.Kuroki, P.Wang. The lower and upper approximations in a Fuzzy Group. Information Sciences,1996,90(1-4):203-220
[60]王国俊.论Fuzzy格的构造.数学学报,1986.29(2)：1083
[61]Q. Sun, Z.Zhang, J. Liu. Soft sets and soft modules. Rough Sets and Knowledge Technology. Lecture Notes in Computer Science,2008,5009:403-409
[62]Y. B. Jun. Soft BCK/BCI-algebras. Computers and Mathematics with Applications, 2008,56(5):1408-1413
[63]P. K. Maji, A. R. Roy, R. Biswas. An application of soft sets in a decision making problem. Computers and Mathematics with Applications,2002,44(8-9):1077-1083
[64]D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang. The parameterization reduction of soft sets and its applications. Computers and Mathematics with Applications,2005,49(5-6): 757-763
[65]I.DAuntsh, M. Winter. Rough Relation Algebras Revisited. Fundamenta Informaticae, 2006,74(2-3):283-300
[66]R.Biswas, S.Nanda. Rough groups and rough subgroups. Bulletin of the Polish Academy os Sciences Mathematics,1994,42(3):251-254
[67]M.I.Ali. A note on soft sets, rough soft sets and fuzzy soft sets. Applied Soft Computing, 2011,11(4):3329-3332
[68]Y. She, G. Wang. An axiomatic approach of fuzzy rough sets based on residuated lattices. Computers and Mathematics with Applications,2009,58(1):189-201
[69]N.Kuroki. Rough ideals in Semigroups. Information Sciences,1997,100(1):139-163
[70]B.Dawaz, M.Mahdavipour. Roughness in modules. Information Sciences,2006,176 (24):3658-3674
[71]Q.Xiao, Z.Zhang. Rough prime ideals and rough fuzzy prime ideals in semigroups. Information Sciences,2006,176(6):725-733
[72]J.Jiang, C.Wu, D.Chen. The product structure of fuzzy rough sets on a group and the rough T-fuzzy group. Information Sciences,2005,175(1):97-107
[73]Y. Xu, D. Ruan, K. Qin, J. Liu. Lattice-valued logic. Springer-Verlin Berlin Heidelberg New York,2003
[74]P.K.Maji, R.Biswas, A.R.Roy. Fuzzy soft sets. Journal of Fuzzy Mathematics,2001, 9(3):589-602
[75]P.K.Maji, R.Biswas, A.R.Roy. Intuitionistic fuzzy soft sets. Journal of Fuzzy Mathematics,2001,9(3):677-691
[76]X.Yang, T.Lin, J.Yang. Combination of interval-valued fuzzy set and soft set. Computers and Mathematics with Applications,2009,58(3):521-527
[77]W.Xu, J.Ma, S.Wang, G.Hao.Vague soft sets and their properties. Computers and Mathematics with Applications,2010,59(2):787-794
[78]G.Deschrijver, E.E.Kerre. On the relationship between some extensions of fuzzy set theory. Fuzzy Sets and Systems.2003,133(2):227-235
[79]张文修,姚一豫,梁怡.粗糙集与概念格[M].西安交通大学出版社,2006
[80]张文修,吴伟志.粗糙集理论介绍和研究综述.模糊系统与数学,2000,14(4)：1-12
[81]张文修,梁怡,吴伟志.信息系统与知识发现.科学出版社,2003
[82]王宗军,李红侠,邓晓岚.粗糙集理论研究的最新进展及其发展趋势.武汉理工大学学报,2006,28(1)：43-47
[83]胡可云,陆玉昌,石纯一.粗糙集理论及其应用进展.清华大学学报(自然科学版),2001,41(1)：64-68
[84]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.科学出版社,2001
[85]杨孝先,杨坚.信息论基础.中国科学技术出版社,2011
[86]Z.Kong, L.Gao, L.Wang. Comment on "A fuzzy soft set theoretic approach to decision Making problems". Journal of Computational and Applied Mathematics,2009,223(2): 540-542
[87]Y.Jiang, Y. Tang, Q. Chen. An adjustable approach to intuitionistis fuzzy soft sets based decision making. Applied Mathematical Modelling,2011,35(2):824-836
[88]Fu Li.Soft lattices.Global Journal of Science Frontier Research,2010,10(4):157-159
[89]Y.B.Jun, C.H.Park. Applications of soft sets in ideal theory of BCK/BCI-algebras. Information Sciences,2008,178(11):2466-2475
[90]曲炜,朱诗兵.信息论基础及应用.清华大学出版社,2005
[91]Q.Xiao, Z. Zhang. Rough prime ideals and rough fuzzy prime ideals in semigroups. Information Sciences,2006,176(6):725-733
[92]N.Kuroki. Rough ideals in semigroups. Information Sciences,1997,100(1-4):139-163
[93]O.Kazanc, B.Davvaz. On the structure of rough prime (primary) ideals and rough fuzzy prime (primary) ideals in commutative rings. Information Sciences,2008,178(5):1343-1354
[94]周荫清.信息论基础.第3版.北京航空航天大学出版社,2006
[95]B.Dawaz. A short note on algebraic T-rough sets. Information Sciences,2008,178(16): 3247-3252
[96]B.Dawaz. Roughness in rings. Information Sciences,2004,164(1-4):147-163
[97]B.A.Davey, H.A.Priestley. Introduction to Lattices and Order, seconded. Cambridge University Press,2002
[98]丁晶,王文圣,赵永龙.以互信息为基础的广义相关系数.四川大学学报(工程科学版),2002,34(3)：1-5
[99]X.Yuan, C.Zhang, Y.Ren.Generalized fuzzy groups and many valued applications. Fuzzy Sets Syst.,2003,138(1):205-211
[100]P.Majumdar, S.K.Samanta. On soft mapping. Computers and Mathematics with Applications,2010.60(9):2666-2672
[101]F. Feng, C.Li, B. Davvaz, M.I.Ali. Soft sets combined with fuzzy sets and rough sets:a tentative approach[J]. Soft Computing,2010,14(9):899-911
[102]陆钟万.面向计算机科学的数理逻辑.第二版.科学出版社,2001
[103]向大晶,刘先平.关于软集理论的综述报告.湖北民族学院学报(自然科学版)2009,27(4)：361-365
[104]李龙澍,张霞.基于Rough集的情报检索研究.情报学报,2002,21(1)：35-45
[105]王灿辉,张敏,马少平.自然语言在处理信息检索中的应用综述.中文信息学报.2007,21(2)：35-45
[106]何薇,徐伟华.信息检索的粗糙集方法.重庆理工大学学报(自然科学),2010,24(9)：84-88
[107]张莉.城市交通拥堵问题研究.决策,,2011,5：6-7
[108]刘晓.关于城市交通拥堵问题研究的文献综述.经济研究导刊,,2010,4：102-103
[109]张元亮,卢鹏.基于粗糙集的城市交通拥堵预警算法分析.交通科技与经济,2009,2：74-76
[110]刘智勇.智能交通控制理论及其应用[M].科学出版社,,2003
[111]王炜,过秀成.交通工程学[M].东南大学出版社,2000
[112]陆化普.城市交通现代化管理[M].人民交通出版社,,2002
[113]宁宣熙.堵塞流理论及其应用[M].科学出版社,2002
[114]席建锋,王晓燕,王双维,王艳玲,邓毅萍.基于粗糙集的道路交通事故成因层次分析方法.长春理工大学学报(自然科学版),2009,32(2)：257-259
[115]张鹏,张靖,刘玉增.粗糙集在交通事故黑点成因分析中的应用[J].电子科技大学学报,2007,36(2)：267-270
[116]姚智胜,邵春福,龙德璐.基于粗糙集理论的路段交通事故多发点成因分析[J].中国安全科学学报,2005,15(12)：107-109
[117]张一鸣,尹春桥,王鹏.基于模糊推理的交通拥堵判断方法.西部交通科技,2012,02：64-69
[118]李兰冰.我国城市交通拥挤的成因及其对策研究[J].理论学刊,2005,6：33-35
[119]冯金巧.城市道路交通拥挤预测关键技术研究.吉林大学博士毕业论文,2008
[120]美国智能交通信息发展及现状[R].北京交管局赴美智能交通管理培训组技术报告.2002：2-10
[121]王振强.城市交通拥堵问题及解决途径[J].甘肃科技,2002,18(10)：1-7
[122]张晓亮,张可,刘浩,李静,陈希.基于FCM-粗糙集的城市快速路交通状态判别.系统工程，2010,28(8)：74-80
[123]刘浩,张晓亮,张可.基于粗糙集交通信息提取计算的城市道路行程时间预测.公路交通科技,2008,25(10)：117-122
[124]吴宏娜,韩印.城市交通拥挤事件的模糊C均值聚类识别方法研究.价值工程,,2011,(07)：184-186
[125]白俊鸽,陈阳,赵良.基于数据挖掘的交通状态决策信息融合模型的研究.硅谷,2010,(15)：92
[126]马世勇,郑应文,柯栋忠.城市交通拥堵自动判别方法研究.福建电脑,2007,(1)：7-8
[127]李林波,万艳花,张勇平.城市交通拥挤发生的一般规律[J].山东交通学院学报,2004,12(1)：55-59
[128]谈晓洁,周晶,盛昭瀚.城市交通拥挤特征及疏导决策分析[J].管理工程学报,2003,17(1)：56-60
[129]姜桂艳.道路交通状态判别技术与应用[M].北京：人民交通出版社,2004
[130]戢晓峰,刘澜,吴其刚.区域路网交通信息提取方法.西南交通大学学报,2008,43：422-426
[131]刘静,关伟.交通流预测方法综述.公路交通科技,2004,21(3)：82-85
[132]陆化普,李瑞敏,朱茵.智能交通系统概论[M].北京：中国铁道出版社,2004
[133]贺国光,马寿峰,李宇.基于小波分解与重构的交通流短时预测法[J].系统工程理论与实践,2002,(9)：101-106
[134]杜豫川,周小鹏,郝媛,孙立军.基于实时检测数据的交通拥堵检测算法研究.第二届中国智能交通年会论文集,2006,313-316
[135]B.L.Brian, M.J.Demertsky. Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. Systems,Man and Cybernetics, 1994. Humans, Information and Technology,1994 IEEE International Conference on (2) 1706-1709
[136]张黎.交通流预测评价中如何运用模糊数学方法进行预测.科技向导,2010,(27)：185-186
[137]卫寒梅,郑应文.基于粗糙集和D-S证据理论的交通拥堵预警.福州大学学报(自然科学版),2008,,36(1)：69-72
[138]Z.Kong, L.Gao, L. Wang, S. Li, The normal paremeter reduction of soft sets and its algorithm.Computers and Mathematics with Applications.2008,56,3029-3037
[139]A.R.Roy, P. K. Maji, A fuzzy soft set theoretic approach to decision making problems. Journal of Computational and Applied Mathematics.2007,203:412-418
[140]Lei Fan, William W.Wilson, BruceDahl. Congestion, port expansion and spatial competition for US container imports. Transportation Research Part E,2012,48:1121-1136
[141]J.Wang, L.Wang. Congestion analysis of traffic networks with direction-dependant heterogeneity. Physica A,2013,392:392-399
[142]H. Li, Daniel J. Graham, Arnab Majumdar. The effects of congestion charging on road traffic casualties:A causal analysis using difference-in-difference estimation. Accident Analysis and Prevention,2012,49:366-377