• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

    2019-11-07 03:12:36NingCaoShengfangLiKeyongShenShengBinGengxinSunDongjieZhuXiuliHanGuangshengCaoandAbrahamCampbell
    Computers Materials&Continua 2019年10期

    Ning Cao,Shengfang LiKeyong ShenSheng Bin,Gengxin Sun,,Dongjie Zhu,Xiuli Han,Guangsheng Cao and Abraham Campbell

    Abstract:Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

    Keywords:Origin-destination(OD)flows,semantics analytics,complex network,big data analysis.

    1 Introduction

    In recent years,with the development of up-to-date technology in wireless network communication,such as 5G and Global Position System,a dramatic rise of crowd sensed data collecting and processing had been seen.Analytics of sensing data has been widely used to enable a broad spectrum of applications,ranging from city planning[Horner and O"Kelly(2001)]or traffic[Kitamura,Chen,Pendyala et al.(2000);Lakhina,Mark,Christophe et al.(2005)]to epidemic disease monitoring[Colizza,Barrat,Barthelemy et al.(2007);Hufnagel,Brockmann and Geisel(2004)]or real-time reporting from disaster situations[Li,Li,Chen et al.(2018)].

    In the field of mobile crowd sensing,for example,cellphones,vehicular sensors,or people themselves collected information.Hence,the obtained data through using crowd sensing methods is a new trend for big data acquisition[Sun and Bin(2017)].Position information would become a type of core data for constructing smart vehicles[Pan,Xu,Wu et al.(2011);Wu,Wu,Cheng et al.(2007)].These core data can form position-based social networks[Song,Hu,Leng et al.(2015)].

    The most important position-based social networks which stand for behavior of crowds in a town are origin-destination flows.It describes a journey by its departure point(Origin)and arrival point(Destination)[Sun and Bin(2018)].OD flows not only can reflect people'behavior but also traffic jam.However,a major challenge for broader adoption of these patterns under OD flows is that the sensed data is not always reliable[Han,Dai,Paritosh et al.(2016)].Taxi is acted as the most frequently used means of transportation,its tracks can be accurately recorded with the help of GPS.So it is a very appropriate data for gathering and evaluating OD flows.

    We firstly build a taxi flow complex network by GPS tracks and detect some distinctive and implicit patterns through detecting community structure[Bin and Sun(2011)].Then we use a novel complex network model to build a complex network[Shao and Sui(2014)]through compounding POI network and public transport network to OD flows network.Based on the composited complex network,spatiotemporal analysis is done to those patterns and discovers that there are close relationships between the semantics of OD flows and those patterns.At last,we design a new method to analyze semantics of OD flows through multiple relationships,and the new method is verified on actual dataset.

    Our contribution lies on the following two aspects:Firstly,a novel method to evaluate the OD flows between geographical positions is proposed.We use multi-subnet composited complex network model to express multiple kinds of actual impact factors for OD flows in a city.Secondly,through topological analytics of the composited complex network,we discover that there are distinctive patterns which have tight relations with semantics of OD flows.Through spatiotemporal analysis,geographical location of boarding and disembarking can be discovered.Combined with POIs and public transport lines,we can get more accurate semantics of OD flows.

    2 Related work

    Research on taxi trajectory for understanding people behavior in location-based social networks is a very active research field at present.There had been many related research results.

    Yuan et al.[Yuan,Zheng,Xie et al.(2012)]presented a decision model for statistical analysis of the dataset of taxi trajectory,the model can predict the passenger flow of taxis.Ying et al.[Ying,Kuo,Tseng et al.(2014)]proposed a new algorithm that depends on historical data to compute the shortest path for a given departure position and arrival position.Zhang et al.[Zhang,Sun,Li et al.(2015)]proposed a data mining algorithm to find abnormal driving behavior based on taxi's tracks,it can be used to automatically detect dangerous driving behavior or traffic jam.Chang et al.[Chang,Tai and Hsu(2009)]proposed a taxi passenger flow forecasting model based on multiple demand factors.Based on historical data,the model can successfully predict passenger demand in different time periods.

    Human travel behavior had tight relationship with social data.Li et al.[Li,Wu,Xu et al.(2014)]studied taxi users' social network information,and they found the intrinsic relationship between taxi trajectory and users' sharing of social network information.The most major function of taxi tracks research is detecting urban areas of different roles in a town.Zhong et al.[Zhong,Huang,Stefan et al.(2014)]investigated the relation between the location of users getting on and getting off and the function of urban areas.Zheng et al.[Zheng,Capra,Wolfson et al.(2014)]designed a method which maybe detect various functional areas of a town through using points of interests.

    3 Preliminaries

    This section introduces compounding mapping operation and subnet compounding operation of multi-subnet composited complex network model.

    Definitions 1(Compounding mapping):Given subnet networkGa=(Va,Ea,Ra,Fa),Gb=(Vb,Eb,Rb,Fb),R′is called as set of compounding interrelations,r′∈R′,Ψ:V1×V2→r′is called as compounding mapping betweenG1andG2according tor′,which is called as compounding relation.R′is called as set of compounding relations.

    Definitions 2(Subnet compounding):Given subnet networkGa=(Va,Ea,Ra,Fa),Gb=compounding mapping Ψ:V1×V2→r′,r′∈R′,compounding subnetG1toG2would generate a new composited one networkG=(V,E,R,F),

    An example of subnet compounding is illustrated in Fig.1.

    Figure 1:Subnet compounding of multiple network(G1,G2,G3,G4)

    4 Dataset description

    Firstly,the taxi trace dataset provided by Transportation Committee of Qingdao city is introduced.The dataset with about 20 million taxi-GPS records consists of 5872 taxi and covers 371 days.State of taxi is defined in a predetermined time interval of one minute,the state includes some fields as follows:

    ●ID:the identification of data record;

    ●GPS LONGITUDE:longitude of a record;

    ●GPS LATITUDE:latitude of a record;

    ●LADEN/UNLADEN STATE:whether a taxi is laden at sampled time,1 represents it is laden and 0 represents it is unladen;

    ●TIME:the sampled time.

    An example of state explanation is show in Tab.1.

    Table 1:An example of state explanation

    Abnormal data cleaning process is a necessary step in big data analysis.We remove taxi traces whose length is less than 500 m and more than 30 km or travel time less than 2 mins.

    5 Spatiotemporal study and pattern analysis

    For the purpose of analysis,Qingdao urban map is divided into cells of 0.5×0.5 km2.To estimate the OD flows,we count the quantity of taxi traces from position Lito position Lj.The quantity of taxi traces cijcan be approximated as OD flow between position Liand position Lj.Through statistical analysis,we found that cijis rather uneven.Statistical analysis indicates that most of human behavioral activities by taxi can be reflected by OD flows.The quantity of OD flows whose cijvalue is more than1000 per month is 237,and the quantity of grids bound up with those 237 OD flows is 75.We think that they can represent typical human behavior by taxi.

    We use the 75 location grids as nodes and those 237 OD flows as edges to build a complex network,which is shown as Fig.2.

    Figure 2:The complex network of OD flows

    For the complex network,we use Mapping Vertex into Vector algorithm to detect community structure.Nodes of the complex network are divided into three communities(green grids,red grids,orange grids)as shown in Fig.3.

    Figure 3:Distribution of grids belonged to three communities in Qingdao urban map

    For better understanding OD flows and identifying emerging patterns,then we explore spatial and temporal distribution of OD flows.

    According to the LADEN/UNLADEN STATE and TIME in source dataset,we can get taxi demands variation trend varying time.The taxi demands with hours in a day is shown in Fig.4.

    Figure 4:Percentage of laden taxis according to the hours of day

    As expected,the percentage of laden taxis varies with working hours.It begins to increase sharply from 7:00,it will gradually reach peak value between17:00 and 19:00,then it will slowly fall back at night.

    Percentage of taxi traces over time of the day and over weekday and weekend are individually shown in Fig.5 and Fig.6.

    Figure 5:Percentage of taxi traces over time of the day

    From Fig.5 we can see that the percentage of taxi traces over time of the day also follows the business hours,time interval from 7 a.m.to 8 a.m.,and from 4 p.m.to 5 p.m.form two peaks.The result is basically consistent with laden taxis variation.

    From Fig.6 we can see that there are more taxis carrying passengers on weekdays than on weekends.

    Figure 6:Percentage of taxi traces over time of weekday and weekend

    We use vertex in-degree and out-degree of complex networks[Barabási and Albert(1999)]to identify some major locations.The top-10 largest in-degree and out-degree of grid locations is shown in Fig.7 and Fig.8.

    Figure 7:The top-10 largest in-degree of grid locations

    Fig.7 presents the major locations of taxi drop-offs distribution in Qingdao,these locations mainly includes downtown(C,G,H),hospitals(A,E,J),governments(B,F,I)and university(D).

    Figure 8:The top-10 largest out-degree of grid locations

    Fig.8 presents the major locations of taxi pick-ups distribution in Qingdao,these locations mainly include Central Business Districts and large residential districts.

    The related stopping grid positions are shown as Fig.9,where the thickness of links stands for intensity between two grid positions.

    Figure 9:The related taxi stopping positions

    6 OD flows semantics mining method

    POIs are grouped into seven categories including downtown,education,health facilities,public transport hub,central business districts,governments and residential district.

    Percentage of POIs Categories is shown as Fig.10.

    Figure 10:Percentage of POIs Categories

    Fig.11 shows the POI distribution for distinguishing the main POI on each position grid.

    Figure 11:Predominant POI category on each location grid

    We use multi-subnet composited complex network model to compound OD flows network and POI network.Then the semantics of OD flow is defined by the semantics of its starting position grid and ending position grid,such as residential district to public transport hub or central business districts to governments.Through topological analytics of the composited complex network,the quantity of OD flows with each kind of semantics is shown in Tab.2.

    Table 2:Quantity of each semantics

    We select 3 representative semantics to explore their relations with behavioral patterns.

    Figure 12:Percentage of OD flow from residential district to central business districts and OD flow from central business districts to residential district

    From Fig.12 we can see that the OD flow from residential district to central business districts has a peak from 8:00 a.m.to 9:00 a.m.and the OD flow from central business districts to residential district has a peak value from 16:00 to 17:00.The two patterns are in accordance with daily behavior experience which people go to work in the morning and return home in the evening.

    From Fig.13 we can see that residential district to health facilities OD flow is flat distributed in day-time.It means that there are no peaks for some OD flows.Based on the analysis mentioned above,the percentage of OD flows with hours distribution can be acted as their feature to identify these OD flows.So,we could explain each OD flow by using a feature vectorSd.

    So,in like manner,we could explain each OD flow with another feature vectorWd.

    We have divided the primary 75 location grids into three communities,there are dense OD flows in the same community,and there are sparse OD flows between two communities.To analyze the empirical observation,we use multi-subnet composited complex network again to compound public transportation network to the former composited network.The public transport network consists of 873 bus station nodes and 1522 lines between bus stations,its topology is shown as Fig.14.

    Figure 14:The complex network of Qingdao public transport

    We found that the more there are public transport lines between two grid locations,the less there are OD flows between them.Distance is not the most important factor of OD flows.An example is shown in Fig.15.

    Figure 15:Relationship between OD flow and public transport line

    From Fig.15 we can see that the distance of grid A-grid B and he distance of grid A-grid C are almost the same,but there are much more OD flows between grid A and grid B than them between grid A and grid C.It is because that there are public transport stations nearby grid A and grid C.So taking public transport into consideration,it will mine better the semantics of OD flows.

    We use an improved Support Vector Machine[Fung and Mangasarian(2005)]to classify above defined feature vectors.Our experimental dataset is actual taxi trajectory data of Qingdao.The actual dataset is stochastically divided into three subsets,train set accounts for 70%,validation accounts for 20% and test set accounts for 10%.The results are limited to several semantic types shown in Tab.2.The classification process is run 100 times and the accurate rate is shown in Tab.3.

    Table 3:The predictive accuracy for each type of feature vectors

    7 Conclusion

    In this paper,our research pays close attention to the OD flows from taxi-GPS traces and understands crowd movement.Through data gathered in Qingdao,China,the distinctive human behavioral patterns which closely related with OD flows are found.Then,a semantics mining method of OD flows is proposed through compounding Points Of Interests(POI)network and public transport network to OD flows network.Experimental results show that we can mine more accurate unknown rules based on the method.

    Future work includes being able to accurately predict taxi flow,comparing pattern of OD flow under different conditions,and suggesting for urban traffic planning.

    Acknowledgement:This work is supported by Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MG011.This work is also supported by Key Research and Development Program in Shandong Provincial(2017GGX90103).

    欧美97在线视频| 国产一级毛片七仙女欲春2| 午夜福利视频精品| 欧美极品一区二区三区四区| 亚洲欧美成人精品一区二区| 联通29元200g的流量卡| 亚洲久久久久久中文字幕| 亚洲精品色激情综合| 一级片'在线观看视频| 超碰av人人做人人爽久久| 日韩亚洲欧美综合| 免费黄色在线免费观看| 亚洲精品456在线播放app| 国产成人精品一,二区| 日韩在线高清观看一区二区三区| 日韩,欧美,国产一区二区三区| 久久久久国产网址| 免费无遮挡裸体视频| 人人妻人人澡人人爽人人夜夜 | 亚洲国产色片| 中文字幕制服av| 久久久a久久爽久久v久久| 网址你懂的国产日韩在线| 97人妻精品一区二区三区麻豆| 美女xxoo啪啪120秒动态图| 在现免费观看毛片| 国产乱人偷精品视频| 伦理电影大哥的女人| 亚洲精品成人久久久久久| 国产黄色小视频在线观看| 天堂俺去俺来也www色官网 | 国产男人的电影天堂91| 在线免费十八禁| 欧美97在线视频| 国内精品美女久久久久久| 亚洲精品乱久久久久久| 午夜爱爱视频在线播放| 日日摸夜夜添夜夜添av毛片| 久久99精品国语久久久| 国产av不卡久久| 99久久精品国产国产毛片| 欧美极品一区二区三区四区| 免费无遮挡裸体视频| 久久精品久久久久久噜噜老黄| 久久97久久精品| 极品教师在线视频| 国产精品一区二区在线观看99 | 免费在线观看成人毛片| 国产激情偷乱视频一区二区| 22中文网久久字幕| 男女国产视频网站| 亚洲精品456在线播放app| 国产探花极品一区二区| 久久这里只有精品中国| 18禁裸乳无遮挡免费网站照片| 精品亚洲乱码少妇综合久久| 九九在线视频观看精品| 久久久久久久久久黄片| 国产麻豆成人av免费视频| 久久草成人影院| 精品一区在线观看国产| 爱豆传媒免费全集在线观看| 国产成人免费观看mmmm| 草草在线视频免费看| 99久久中文字幕三级久久日本| 国产国拍精品亚洲av在线观看| 国产精品人妻久久久影院| 2018国产大陆天天弄谢| 亚洲av男天堂| 美女黄网站色视频| 精品国产三级普通话版| 菩萨蛮人人尽说江南好唐韦庄| 亚洲av成人精品一二三区| 又大又黄又爽视频免费| av免费在线看不卡| 亚洲18禁久久av| 成人漫画全彩无遮挡| 六月丁香七月| 51国产日韩欧美| 日韩电影二区| 久久久午夜欧美精品| 日韩强制内射视频| a级毛色黄片| 国产伦在线观看视频一区| 深夜a级毛片| 国产av国产精品国产| 最近中文字幕高清免费大全6| 啦啦啦啦在线视频资源| 欧美最新免费一区二区三区| 99久久中文字幕三级久久日本| 国产亚洲av嫩草精品影院| 99热这里只有是精品50| 日本熟妇午夜| 少妇熟女aⅴ在线视频| 精品少妇黑人巨大在线播放| 午夜免费男女啪啪视频观看| 少妇的逼水好多| 欧美日本视频| 97超碰精品成人国产| 国产精品99久久久久久久久| 国产伦在线观看视频一区| 插逼视频在线观看| 亚洲精品乱码久久久久久按摩| 人人妻人人澡欧美一区二区| 日韩不卡一区二区三区视频在线| 高清av免费在线| 日韩av在线大香蕉| 能在线免费观看的黄片| 精品99又大又爽又粗少妇毛片| 小蜜桃在线观看免费完整版高清| 日韩av在线免费看完整版不卡| 欧美性猛交╳xxx乱大交人| 超碰97精品在线观看| 岛国毛片在线播放| 国产男人的电影天堂91| 成人二区视频| 波野结衣二区三区在线| 美女黄网站色视频| 日本黄色片子视频| 精品国产露脸久久av麻豆 | 男女视频在线观看网站免费| 又黄又爽又刺激的免费视频.| 日本黄大片高清| 一级a做视频免费观看| 久久精品国产亚洲网站| 久久鲁丝午夜福利片| 国内揄拍国产精品人妻在线| 春色校园在线视频观看| 人妻一区二区av| 黄片wwwwww| 成人无遮挡网站| 中文字幕久久专区| 国产av在哪里看| 亚洲欧美日韩卡通动漫| 啦啦啦韩国在线观看视频| 久久久午夜欧美精品| 两个人视频免费观看高清| 国产在线一区二区三区精| 天堂中文最新版在线下载 | 69av精品久久久久久| 内地一区二区视频在线| 永久网站在线| 69人妻影院| 精品亚洲乱码少妇综合久久| 精品人妻偷拍中文字幕| 女人十人毛片免费观看3o分钟| 欧美高清性xxxxhd video| av.在线天堂| 一级毛片电影观看| 国产在视频线在精品| 久久久久久九九精品二区国产| 亚洲av电影在线观看一区二区三区 | 18禁在线无遮挡免费观看视频| 国产免费又黄又爽又色| 亚洲精品成人久久久久久| 国产在视频线精品| 又黄又爽又刺激的免费视频.| 三级经典国产精品| 日韩中字成人| 丝袜美腿在线中文| 欧美潮喷喷水| 国内揄拍国产精品人妻在线| 男人和女人高潮做爰伦理| 联通29元200g的流量卡| 婷婷六月久久综合丁香| 97精品久久久久久久久久精品| 内射极品少妇av片p| 亚洲伊人久久精品综合| 日本一二三区视频观看| 日本三级黄在线观看| 亚洲性久久影院| 插逼视频在线观看| 汤姆久久久久久久影院中文字幕 | 国产综合精华液| av卡一久久| 午夜激情福利司机影院| 国产精品蜜桃在线观看| 内地一区二区视频在线| 亚洲人成网站在线观看播放| 精品国产一区二区三区久久久樱花 | 久久久精品免费免费高清| 国产一区二区三区综合在线观看 | 午夜福利视频1000在线观看| 麻豆乱淫一区二区| 嫩草影院精品99| 高清欧美精品videossex| 最近手机中文字幕大全| 啦啦啦啦在线视频资源| 国产精品人妻久久久久久| 中文字幕av成人在线电影| 黄片wwwwww| 国国产精品蜜臀av免费| 最后的刺客免费高清国语| 国产精品麻豆人妻色哟哟久久 | 久久国产乱子免费精品| 美女xxoo啪啪120秒动态图| 欧美成人精品欧美一级黄| 国产成人午夜福利电影在线观看| 日本免费在线观看一区| 亚洲国产精品专区欧美| 亚洲高清免费不卡视频| 欧美丝袜亚洲另类| 女的被弄到高潮叫床怎么办| 国产午夜精品一二区理论片| 国产免费福利视频在线观看| 久久久成人免费电影| 一级二级三级毛片免费看| 日韩 亚洲 欧美在线| 国产淫语在线视频| 国产高潮美女av| 国产成人精品婷婷| 久久久久精品性色| 欧美潮喷喷水| 亚洲av国产av综合av卡| 久久99蜜桃精品久久| 精品一区二区三卡| 欧美日韩一区二区视频在线观看视频在线 | 少妇猛男粗大的猛烈进出视频 | 国产精品国产三级专区第一集| 日韩一本色道免费dvd| 精华霜和精华液先用哪个| 国产男人的电影天堂91| 精品欧美国产一区二区三| 欧美日韩亚洲高清精品| 久久亚洲国产成人精品v| 男人舔女人下体高潮全视频| 午夜爱爱视频在线播放| 国产白丝娇喘喷水9色精品| 久久精品综合一区二区三区| 伊人久久国产一区二区| 久久精品国产自在天天线| 在线免费十八禁| www.av在线官网国产| 国产一区二区亚洲精品在线观看| 天堂影院成人在线观看| 少妇丰满av| 最近的中文字幕免费完整| 国产白丝娇喘喷水9色精品| 国产精品爽爽va在线观看网站| 亚洲国产成人一精品久久久| 久久久久久久久久久免费av| 人人妻人人看人人澡| 日韩欧美一区视频在线观看 | 日韩人妻高清精品专区| 国产精品一及| 欧美激情在线99| 亚洲精品456在线播放app| 国产一区二区三区综合在线观看 | 最近最新中文字幕免费大全7| 最近手机中文字幕大全| 日韩视频在线欧美| 久久热精品热| 18禁在线播放成人免费| 人妻系列 视频| 久久久久久久久久人人人人人人| 欧美一区二区亚洲| 国产免费又黄又爽又色| 丰满人妻一区二区三区视频av| 久久草成人影院| 国产乱人视频| 亚洲国产色片| 身体一侧抽搐| 亚洲va在线va天堂va国产| 亚洲欧美日韩东京热| 久久精品综合一区二区三区| 国产日韩欧美在线精品| 色吧在线观看| 精品亚洲乱码少妇综合久久| 看非洲黑人一级黄片| 最近的中文字幕免费完整| 欧美日韩一区二区视频在线观看视频在线 | 亚洲精品乱久久久久久| 伊人久久国产一区二区| 久久国产乱子免费精品| 免费看av在线观看网站| 国产精品一区二区性色av| 亚洲精品影视一区二区三区av| 亚洲国产日韩欧美精品在线观看| 天堂√8在线中文| 国产久久久一区二区三区| 国产三级在线视频| 国产成人aa在线观看| 国产精品久久久久久久电影| 国产高清有码在线观看视频| 中文资源天堂在线| 中国国产av一级| 欧美97在线视频| www.色视频.com| 18禁动态无遮挡网站| 亚洲精品亚洲一区二区| 国产麻豆成人av免费视频| 免费观看av网站的网址| 亚洲综合精品二区| 国产黄片美女视频| 日韩av在线大香蕉| 日韩国内少妇激情av| or卡值多少钱| 日韩在线高清观看一区二区三区| 国产av不卡久久| 老女人水多毛片| 国产黄频视频在线观看| 青春草国产在线视频| 午夜福利高清视频| 国产成人aa在线观看| 国产午夜福利久久久久久| 18禁在线无遮挡免费观看视频| 国产成人freesex在线| 国产高清国产精品国产三级 | 日韩一区二区三区影片| 中文字幕av成人在线电影| 男人和女人高潮做爰伦理| 美女脱内裤让男人舔精品视频| 91久久精品国产一区二区成人| 亚洲精华国产精华液的使用体验| 久久久精品94久久精品| 久久久久久久久中文| xxx大片免费视频| 国产在视频线在精品| 男人舔女人下体高潮全视频| 中文字幕亚洲精品专区| 免费观看a级毛片全部| a级毛色黄片| 日韩人妻高清精品专区| 午夜激情福利司机影院| 久久精品人妻少妇| 久99久视频精品免费| 日本黄大片高清| 高清欧美精品videossex| 欧美日韩综合久久久久久| 91久久精品国产一区二区成人| 一个人看视频在线观看www免费| 国产成人91sexporn| 午夜激情欧美在线| 亚洲最大成人中文| 街头女战士在线观看网站| 国精品久久久久久国模美| 九九爱精品视频在线观看| 免费观看av网站的网址| 听说在线观看完整版免费高清| 人妻系列 视频| 91狼人影院| 亚洲国产精品专区欧美| 亚洲成人久久爱视频| 久久鲁丝午夜福利片| 国产精品av视频在线免费观看| 啦啦啦啦在线视频资源| 亚洲国产精品sss在线观看| 韩国高清视频一区二区三区| 自拍偷自拍亚洲精品老妇| 秋霞在线观看毛片| av在线观看视频网站免费| 亚洲成人久久爱视频| 日韩欧美 国产精品| 国产精品熟女久久久久浪| 国产精品久久久久久久电影| 国内精品宾馆在线| 国内精品美女久久久久久| 国内精品宾馆在线| 国产成人精品久久久久久| 高清欧美精品videossex| 九九在线视频观看精品| 97热精品久久久久久| 亚洲色图av天堂| 亚洲国产精品成人综合色| 白带黄色成豆腐渣| 精品久久久久久久末码| 精品国产露脸久久av麻豆 | 亚洲欧美成人精品一区二区| 男女国产视频网站| 亚洲性久久影院| 欧美一区二区亚洲| www.av在线官网国产| 久久97久久精品| 久久久久国产网址| 免费观看无遮挡的男女| 国产白丝娇喘喷水9色精品| 色播亚洲综合网| 色综合色国产| 午夜福利在线观看吧| 大陆偷拍与自拍| 国产精品久久视频播放| 肉色欧美久久久久久久蜜桃 | 免费少妇av软件| 国产精品一区二区三区四区久久| av在线老鸭窝| 日本猛色少妇xxxxx猛交久久| 欧美97在线视频| 精品久久久久久久久av| 国产精品人妻久久久久久| 99九九线精品视频在线观看视频| 欧美+日韩+精品| 国产黄片视频在线免费观看| 精品酒店卫生间| 亚洲婷婷狠狠爱综合网| 在线天堂最新版资源| 日本一二三区视频观看| 两个人的视频大全免费| 最近的中文字幕免费完整| 亚洲精品日韩在线中文字幕| 亚洲精品456在线播放app| 少妇被粗大猛烈的视频| 又爽又黄a免费视频| 97超视频在线观看视频| 啦啦啦韩国在线观看视频| 一级毛片电影观看| 国产精品99久久久久久久久| 91精品伊人久久大香线蕉| 白带黄色成豆腐渣| 高清欧美精品videossex| 国产一级毛片七仙女欲春2| 亚洲国产色片| av在线观看视频网站免费| 亚洲精品中文字幕在线视频 | 亚洲一区高清亚洲精品| 最新中文字幕久久久久| 观看免费一级毛片| 一边亲一边摸免费视频| 中文字幕av在线有码专区| 国产亚洲最大av| 欧美日本视频| 女人十人毛片免费观看3o分钟| av福利片在线观看| 日韩亚洲欧美综合| 免费高清在线观看视频在线观看| 蜜桃亚洲精品一区二区三区| 久久这里只有精品中国| 国产成人a区在线观看| 少妇的逼好多水| 久久久久久久久中文| 青青草视频在线视频观看| 伊人久久精品亚洲午夜| 国产精品久久久久久av不卡| 成年女人看的毛片在线观看| 久久久久精品性色| 日韩av不卡免费在线播放| a级毛色黄片| 看非洲黑人一级黄片| 国产乱人偷精品视频| 亚洲av福利一区| 波多野结衣巨乳人妻| 99九九线精品视频在线观看视频| 精品酒店卫生间| 亚洲综合精品二区| 日韩一本色道免费dvd| 女人被狂操c到高潮| 又大又黄又爽视频免费| 男女那种视频在线观看| 国产亚洲最大av| 不卡视频在线观看欧美| 欧美成人一区二区免费高清观看| 大话2 男鬼变身卡| 少妇人妻精品综合一区二区| 汤姆久久久久久久影院中文字幕 | 日日啪夜夜撸| 婷婷色av中文字幕| 亚洲综合色惰| 啦啦啦韩国在线观看视频| 成人漫画全彩无遮挡| 99久久九九国产精品国产免费| 性色avwww在线观看| 你懂的网址亚洲精品在线观看| 精品一区二区三区视频在线| 亚洲乱码一区二区免费版| 欧美成人精品欧美一级黄| 国产成人91sexporn| 大又大粗又爽又黄少妇毛片口| 乱人视频在线观看| 99久久九九国产精品国产免费| 午夜福利视频1000在线观看| 国产黄色免费在线视频| 亚洲美女搞黄在线观看| 久久人人爽人人爽人人片va| 日韩成人伦理影院| 欧美xxxx黑人xx丫x性爽| 国产免费一级a男人的天堂| 亚洲精品第二区| 97人妻精品一区二区三区麻豆| av黄色大香蕉| 2018国产大陆天天弄谢| 不卡视频在线观看欧美| 午夜福利视频1000在线观看| 汤姆久久久久久久影院中文字幕 | 91aial.com中文字幕在线观看| 午夜精品国产一区二区电影 | av在线天堂中文字幕| 免费无遮挡裸体视频| 在现免费观看毛片| 国产真实伦视频高清在线观看| 伊人久久国产一区二区| 久久久精品94久久精品| 伦理电影大哥的女人| 午夜精品在线福利| 国产一区二区三区av在线| 又爽又黄a免费视频| 欧美另类一区| 国产精品一区二区三区四区免费观看| 国产精品嫩草影院av在线观看| 国产亚洲5aaaaa淫片| 亚洲美女搞黄在线观看| 久久久久久国产a免费观看| 天天一区二区日本电影三级| 一本一本综合久久| 亚洲欧美日韩无卡精品| 亚洲av成人精品一区久久| 亚洲最大成人av| h日本视频在线播放| 午夜爱爱视频在线播放| 久久精品国产自在天天线| 欧美97在线视频| 日本免费a在线| 国产av码专区亚洲av| 国产中年淑女户外野战色| 26uuu在线亚洲综合色| 天堂网av新在线| 国产精品女同一区二区软件| 你懂的网址亚洲精品在线观看| 韩国av在线不卡| 菩萨蛮人人尽说江南好唐韦庄| 国产伦精品一区二区三区四那| 女人十人毛片免费观看3o分钟| 麻豆久久精品国产亚洲av| 99热这里只有是精品在线观看| 久久久久久久久大av| 天堂av国产一区二区熟女人妻| 亚洲aⅴ乱码一区二区在线播放| 日本-黄色视频高清免费观看| 国产69精品久久久久777片| 色综合亚洲欧美另类图片| 国产又色又爽无遮挡免| 好男人视频免费观看在线| 国产乱人视频| 亚洲精品一区蜜桃| av一本久久久久| 国产精品一区二区在线观看99 | 日本-黄色视频高清免费观看| 韩国高清视频一区二区三区| 国产精品国产三级国产av玫瑰| 亚洲无线观看免费| 99久久精品国产国产毛片| 欧美日韩在线观看h| 老师上课跳d突然被开到最大视频| 最近视频中文字幕2019在线8| 人人妻人人澡人人爽人人夜夜 | 国产 一区 欧美 日韩| 综合色av麻豆| 国产高清不卡午夜福利| 久久99热6这里只有精品| 我的女老师完整版在线观看| 午夜精品一区二区三区免费看| 精品久久久久久久久av| 日韩在线高清观看一区二区三区| 搡老乐熟女国产| 国产又色又爽无遮挡免| 有码 亚洲区| 成人午夜精彩视频在线观看| 精品久久久久久久久av| 成人欧美大片| 欧美性感艳星| 久久鲁丝午夜福利片| 天天躁日日操中文字幕| 人体艺术视频欧美日本| 国产在视频线精品| 成人毛片60女人毛片免费| 男人爽女人下面视频在线观看| 成人毛片60女人毛片免费| 国产成人aa在线观看| 2022亚洲国产成人精品| 美女高潮的动态| 国产黄频视频在线观看| 欧美成人精品欧美一级黄| 黄片wwwwww| 亚洲图色成人| 97超视频在线观看视频| 啦啦啦韩国在线观看视频| 精品一区在线观看国产| 欧美激情国产日韩精品一区| 国产黄片视频在线免费观看| 久久久久久久久久久免费av| 最近手机中文字幕大全| 永久免费av网站大全| 国产精品嫩草影院av在线观看| 久久精品人妻少妇| 狠狠精品人妻久久久久久综合| 久久久精品94久久精品| 一级毛片黄色毛片免费观看视频| 国产一级毛片在线| 亚洲国产欧美人成| 国产老妇女一区| 国产精品国产三级国产专区5o| 亚洲自偷自拍三级| 亚洲色图av天堂| a级毛色黄片| 久久人人爽人人爽人人片va| h日本视频在线播放| a级毛色黄片| 一级片'在线观看视频| 亚洲经典国产精华液单| 国产精品一二三区在线看| 亚洲熟妇中文字幕五十中出| 久久久久久久久久久免费av| 青春草国产在线视频| 国产精品一区二区三区四区久久| 国产黄片美女视频| 啦啦啦啦在线视频资源| 精品久久久久久久久av| 欧美日韩综合久久久久久| 99久国产av精品| 亚洲国产欧美人成| 国产精品久久久久久久电影| 亚洲国产成人一精品久久久| 日韩强制内射视频| 精品一区二区三区人妻视频| 欧美成人午夜免费资源| 超碰av人人做人人爽久久| 中文字幕av成人在线电影|