• <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 ?

    Conditional autoregressive negative binomial model for analysis of crash count using Bayesian methods

    2014-09-17 06:00:46XuJianSunLu

    Xu Jian Sun Lu

    (1School of Transportation, Southeast University, Nanjing 210096, China)

    (2Center for Transportation Research, University of Texas at Austin, Austin 78712, USA)(3Department of Civil Engineering, Catholic University of America, Washington DC 20064, USA)

    W ith the increase in the number of vehicles,it is interesting and commendable that currently fatalities are decreasing every year in China,the reason of which can be attributed to the optimization of roadway designs,more safety vehicles,as well as many researches of crashes and the contributing factors.However, still 210 812 reported crashes and 62 387 reported fatalities occurred on roadways in 2011 in China according to official reports[1], demanding the further improvement of transportation safety to reduce the traffic accidents and fatalities.

    The possible access to understand the elements of crashes is to develop statistical analysis methods used to distinguish the significant factors,which can be utilized to provide an optimality criterion to policy makers.During the past several years,numerous methods for analyzing crash counts were proposed[2-6].The earliest approach for crash count data is the Poisson model[7], and then it gives rise to more flexible alternatives, e.g., the negative binomial(NB)model[8], the GIS-based Bayesian approach[9], the finite mixture regression model[10], and the quantile regression method[11].Most of the regression methods applied to model crash counts, however, are focused on aspatial(i.e.non-spatial)analysis.Applied work in aspatial models may not be able to capture spatial heterogeneity and spatial dependence at neighborhood areas, a frequently happening issue in crash counts.This leads to the development of alternative methodologies that focus on spatial modeling in the past few decades.Early pioneering work on spatial modeling is reported by Besag[12], and is further enriched by LeSage et al[13-16].Anselin[17]provided two specifications of spatial models,spatial error model(SEM)(i.e., the spatial autocorrelation model(SAC))and the spatial lag model(SLM)(i.e., the spatial autoregressive model(SAR))that is a special type of conditional autoregressive(CAR)model,at least in a continuous-response setting.

    The primary objective of this study is to develop associations between crash counts on homogeneous segments and the contributing factors,using a negative binomial(NB)-based conditional autoregressive model(CAR)which allows for overdispersion,unobserved heterogeneity and spatial autocorrelation.The Bayesian estimation is employed,using Markov chain Monte Carlo methods and the Gibbs sampler.The independent variables consist of traffic characteristics,roadway design and built environments,and the data are derived from on-system highways of Austin, TX, USA in the year 2010.Meanwhile, the exposure variable and the dummy variable are also considered.

    1 Model Structure

    As described before,there are two specifications of spatial models:the spatial autocorrelation model and the spatial autoregressive model.The general formulation of the spatial autoregressive model for cross-sectional spatial data is

    where yicontains ann×1 vector of dependent variables;ρ is the spatial lag coefficient;W1is the spatial weights matrix;φ is the error term for spatial dependence;xirepresents the matrix of independent variables.

    where λ is the spatial autoregressive coefficient;W2is a known spatial weights matrix like W1,usually containing the first-order contiguity relationships; ε ~N(0,σ2In).The SAR model tends to be difficult to develop for limited-response frameworks,especially when dealing with large scale problems involving a large amount of observations,and yields parameter estimates similar to those estimated from the CAR model.Moreover, due to faster computation,the CAR model is preferred in spatial analysis over the SAR model.Under the MRF assumption, the conditional probability density function of the univariate CAR model is[18]

    The joint probability density function is

    whereEiis the exposure variable,which represents vehicle miles traveled(VMT)in this study;τ denotes an unknown parameter for the exposure measure;β0is the intercept term;βkdenotes the coefficient of thek-th covariate;Xikare indicators for thek-th covariate for segmenti;ψifollows the proper CAR prior,as described before;εiis a random error that has a gamma distribution,that is,εi~ Γ(θ,θ).

    2 Data Description

    In this study,roadways and crash data sets of Austin City in USA in 2010 are used to examine the associations between crash counts on mainlanes and the contributing factors.The roadways in this study are on-system highways, containing interstate highways, US highways,state highways,farm-to-market roadways,etc.In order to avoid the modifiable areal unit problem(MAUP)[19],roadways are split into 1 824 homogeneous segments where geometric characteristics are coincident,as shown in Fig.1.Most segments have a length of 0 to 1.6 km and occupy more than 90%of the whole sample.The average of the segment length on mainlanes is 0.459 km.After merging crashes and segments,1 413 crashes on mainlanes are matched.

    Fig.1 Distribution of homogeneous segments in Austin(Spots are the center points of segments)

    In this study,the dependent variable is the number of crashes,while the exposure variable captures VMT,which is a key crash exposure term(since crash counts closely correlate with VMT,everything else remaining constant),and simply the product of AADT,segment length,and 365 days per year.The dependent variable set contains both continuous and categorical variables,as shown in Tab.1.The indicator for curvature is a dummy variable,that is,if the answer is yes,it equals 1,and 0 otherwise.In addition,traffic characteristics allow for AADT,speed limit,and the percentage of truck AADT.In the past research,environments,especially distances to the nearest hospitals,were rarely employed for the contributing factors to analyze the associations of crash counts.In this study,hospitals are collected for analysis;meanwhile,the distances of which to segments are computed by ArcGIS,as shown in Fig.2.The data of annual rainfall obtained from the US Natural Resources Information System are also collected for analysis.It is noted that it would be best to match the year 2010 crashes to the same year rainfall data,however such information is unavailable,and we cannot find out the data.According to theclimate history in Texas,the annual rainfall changed a little,so 1961—1990 average rainfall is used instead.Fig.3 depicts the distribution of the annual rainfall in Austin.

    Tab.1 Summary statistics of variables for segments

    Fig.2 Distribution of hospitals in Austin

    Fig.3 Distribution of annual rainfall in Austin

    3 Estimation Results and Discussion

    This section discusses the results of the associations between the contributing factors and the crash counts on mainlanes in Austin.Tab.2 shows the parameter estimates of the CAR model for crash counts,based on a total number of 5 000 draws in WinBUGS.

    The association between crash exposure(VMT)and crash rates is estimated to be nonlinear(average exponent τ=0.658 for mainlanes),which follows prior expectations.After controlling the exposure variable(VMT),other covariates regardingcrash rates are estimated,which can be seen in Tab.2.

    Elasticities for total crash counts and fatal crash counts are computed as the average percentage change in the mean crash rate per 1%change in thek-th variable.As shown in Tab.2,crash counts are estimated to have a statistically and practically significant spatial autocorrelation coefficient of 0.624(that is α =0.624).The number of lanes,curve length,AADT per lane,and rainfall have positive impacts on the mean crash rates for mainlanes,while the remaining variables all exhibit negative impacts on the mean crash rates.The elasticity of - 0.123 is found to be that of the curve indicator variables,implying that,holding everything else constant at their means,the mean crash rate is estimated to drop by 0.123 when the indicator variable switches from 0 to 1.The result confirms that the roadway curvature has negative effects on crash rates,which is consistent with the findings of some other studies[5-6].

    Interestingly,the speed limit on mainlanes exhibits negative mean elasticities,implying that higher speed limits are associated with lower mean crash rates,as found in Ref.[4].However,the speed limit has a positive effect on fatality rates,as shown in Tab.2.Rainfall intensity is estimated to be positively associated with crash rates,and an increase of 1%rainfall will result in an increase of 8.622 in crash rates and an increase of 0.283 in fatality rates.As discussed previously,the distances to hospitals rarely appear as contributing factors in the crash modeling literature.It is found that the distances to the nearest hospitals have a negative impact on the mean crash rates,which suggests that shorter distances lead to higher crash rates,however,as expected,positive associations with fatal crash rates(presumably due to more severe collision impacts at higher speeds and time lost in transporting crash victims to an emergency room).

    Tab.2 Estimation results of CAR-NB model for crash and fatal counts

    In this study,the CAR-NB model is compared with another spatial model(CAR-Poisson)and some aspatial models(NB,zero-inflated NB and zero-inflated Poisson),as shown in Tab.3.

    Tab.3 Comparison of results using aspatial models and spatial models

    The deviance information criterion(DIC),as a generalization of the Akaike information criterion(AIC),can be used to compare the goodness-of-fit and complexity of different models estimated under a Bayesian framework.The DIC equation is

    whereD(θˉ)is the deviance evaluated atθˉ which is the posterior mean of the parameters;pDis the effective number of parameters in the model;Dˉ is the posterior mean of the deviance statisticD(θ).With regards to the model superiority and complexity,the lower the DIC,the better the model[20].Tab.3 also presents the log likelihood values,which are used in the likelihood ratio chi-square to test whether all predictors'regression coefficients in the model are simultaneously zero.Meanwhile,Moran'sIis also considered,which is a measure of spatial autocorrelation developed by Moran[21].Negative(positive)values indicate negative(positive)spatial autocorrelation and the values range from -1(indicating perfect dispersion)to+1(perfect correlation).

    It is observed that the CAR-NB model has the lowest DIC and Moran'sIof residuals among these tested models.Meanwhile,mean log likelihood values of the CARNB model are the largest.The statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson,NB,zero-inflated Poisson,zero-inflated NB models due to its lower prediction errors and more robust parameter inference.It can be found that the negative binomial models in Tab.3 are better than the Poisson models due to the fact that overdispersion actually exists in the data.

    4 Conclusions

    1)Statistical tests of DIC,log likelihood and Moran'sIsuggest that the CAR-NB model is preferred over the CAR-Poisson,NB,zero-inflated Poisson,zero-inflated NB models,while the negative binomial models are better than the Poisson models.

    2)The association between crash exposure(VMT)and crash rates is estimated to be nonlinear(average exponent τ =0.658 for mainlanes),with crash rates effectively falling as VMT rises.

    3)The number of lanes,curve length,AADT per lane,and rainfall have positive impacts on crash count,while the remaining variables all exhibit negative impacts.

    4)The distances to the nearest hospitals and the speed limit have negative associations with segment-based crash counts but positive associations with fatality counts,presumably as a result of time loss during transporting crash victims and worsened collision impacts at higher speeds.

    [1]Traffic Management Bureau of the Ministry of Public Security of the People's Republic of China.Road traffic accident statistics annual report of the People's Republic of China(2010)[R].Wuxi:Traffic Management Research Institute of the Ministry of Public Security,2011.(in Chinese)

    [2]Qu X,Guo T,Wang W,et al.Measuring speed consistency for freeway diverge areas using factor analysis[J].Journal of Central South University:Science and Technology,2013,20(1):837-840.(in Chinese)

    [3]Pei Y L,Ma J.Research on countermeasures for road condition causes of traffic accidents[J].China Journal of Highway and Transport,2003,16(4):77-82.

    [4]Ma J,Kockelman K M,Damien P.A multivariate Poisson-lognormal regression model for prediction of crash counts by severity,using Bayesian methods[J].Accident Analysis and Prevention,2008,40(3):964-975.

    [5]Quddus M A,Wang C,Ison S G.Road traffic congestion and crash severity:econometric analysis using ordered response models[J].Journal of Transportation Engineering,2010,136(5):424-435.

    [6]Wang C,Quddus M A,Ison S G.Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model[J].Accident Analysis and Prevention,2011,43(6):1979-1990.

    [7]Jovanis P,Chang H L.Modeling the relationship of accidents to miles traveled[J].Transportation Research Record,1986,1068:42-51.

    [8]Lord D.The prediction of accidents on digital networks:characteristics and issues related to the application of accident prediction models[D].Toronto:University of Toronto,2000.

    [9]Li L,Zhu L,Daniel Z S.A GIS-based Bayesian approach for analyzing spatial-temporal patterns of intra-city motor vehicle crashes[J].Journal of Transport Geography,2007,15(4):274-285.

    [10]Park B J,Lord D.Application of finite mixture models for vehicle crash data analysis[J].Accident Analysis and Prevention,2009,41(4):683-91.

    [11]Qin X,Reyes P.Conditional quantile analysis for crash count data[J].Journal of Transportation Engineering,2011,137(9):601-607.

    [12]Besag J E.Nearest-neighbour systems and the auto-logistic model for binary data[J].Journal of the Royal Statistical Society,Series B:Methodological,1972,34(1):75-83.

    [13]LeSage J P.Spatial econometrics[EB/OL].(1999)[2013-03-15].http://www.spatial-econometrics.com/.

    [14]Miaou S,Song J J,Malick B.Roadway traffic crash mapping:a space-time modeling approach[J].Journal of Transportation and Statistics,2003,6(1):33-57.

    [15]Quddus M A.Modeling area-wide count outcomes with spatial correlation and heterogeneity:an analysis of London crash data[J].Accident Analysis and Prevention,2008,40(4):1486-1497.

    [16]Wang Y,Kockelman K M.A conditional-autoregressive count model for pedestrian crashes across neighborhoods[C/CD]//The92nd Annual Meeting of the Transportation Research Board.Washington DC,USA,2013.

    [17]Anselin L.Spatial econometrics:methods and models[M].Dordrecht:Kluwer Academic Publishers,1988.

    [18]Mariella L,Tarantino M.Spatial temporal conditional auto-regressive model:a new autoregressive matrix [J].Australian Journal of Statistics,2010,39(3):223-244.

    [19]Openshaw S.The modifiable areal unit problem [J].Concepts and Techniques in Modern Geography,1983,38:39-41.

    [20]Spregelhalter D J,Best N G,Carlin B P,et al.Bayesian measures of model complexity and fit[J].Journal of the Royal Statistical Society,Series B:Statistical Methodology,2002,64(4):583-639.

    [21]Moran P A P.Notes on continuous stochastic phenomena[J].Biometrika,1950,37(1):17-23.

    欧美极品一区二区三区四区| 中文资源天堂在线| 在线a可以看的网站| 男的添女的下面高潮视频| 人体艺术视频欧美日本| 人人妻人人澡人人爽人人夜夜| 国产精品一区二区三区四区免费观看| 美女脱内裤让男人舔精品视频| 成年女人看的毛片在线观看| 国产免费一级a男人的天堂| 欧美高清成人免费视频www| 成年免费大片在线观看| 少妇人妻久久综合中文| 丝瓜视频免费看黄片| 王馨瑶露胸无遮挡在线观看| 久久综合国产亚洲精品| 欧美少妇被猛烈插入视频| 国产一区亚洲一区在线观看| 久久人人爽人人爽人人片va| 国产成年人精品一区二区| 国产欧美另类精品又又久久亚洲欧美| 国产一区亚洲一区在线观看| 久久影院123| 69av精品久久久久久| 最近中文字幕高清免费大全6| 国产高潮美女av| 日本一本二区三区精品| 成人鲁丝片一二三区免费| 国产亚洲精品久久久com| 男的添女的下面高潮视频| 免费av观看视频| 国产视频内射| 简卡轻食公司| 少妇的逼水好多| 国产女主播在线喷水免费视频网站| 日韩大片免费观看网站| 欧美xxxx黑人xx丫x性爽| 狠狠精品人妻久久久久久综合| 尾随美女入室| 婷婷色麻豆天堂久久| 精品久久久久久久久av| 亚洲欧美日韩卡通动漫| 精品少妇久久久久久888优播| 亚洲在久久综合| 欧美成人精品欧美一级黄| 国内精品宾馆在线| 精品一区二区三区视频在线| av线在线观看网站| 国产91av在线免费观看| 日本三级黄在线观看| 国产免费又黄又爽又色| 国产中年淑女户外野战色| 观看美女的网站| 国产精品久久久久久精品电影| 国产永久视频网站| 国产成人精品久久久久久| 欧美区成人在线视频| 我的老师免费观看完整版| 黑人高潮一二区| 99久久精品国产国产毛片| 真实男女啪啪啪动态图| 久久久久久九九精品二区国产| 我的女老师完整版在线观看| 久久人人爽人人爽人人片va| 日韩精品有码人妻一区| 亚洲成人av在线免费| 国产在线一区二区三区精| 狂野欧美激情性bbbbbb| 午夜激情福利司机影院| 伦精品一区二区三区| 亚洲熟女精品中文字幕| 国产免费一区二区三区四区乱码| 亚洲最大成人手机在线| 一级毛片久久久久久久久女| 国产精品国产三级专区第一集| 免费人成在线观看视频色| 丝瓜视频免费看黄片| 丝瓜视频免费看黄片| 成人无遮挡网站| 亚洲精品成人久久久久久| 亚洲av日韩在线播放| 白带黄色成豆腐渣| 久久精品国产亚洲av涩爱| 国产男女超爽视频在线观看| 国产高潮美女av| 久久久欧美国产精品| 国产在视频线精品| 黄色怎么调成土黄色| 亚洲精品国产成人久久av| 一区二区三区免费毛片| 午夜激情福利司机影院| 毛片女人毛片| 欧美另类一区| 久久国产乱子免费精品| 中文字幕av成人在线电影| 欧美精品人与动牲交sv欧美| 亚洲怡红院男人天堂| 黑人高潮一二区| 2021天堂中文幕一二区在线观| 精品少妇黑人巨大在线播放| 在线亚洲精品国产二区图片欧美 | 欧美人与善性xxx| 欧美日本视频| 亚洲精品成人av观看孕妇| 97在线视频观看| 在线观看一区二区三区| 美女主播在线视频| 国语对白做爰xxxⅹ性视频网站| 内地一区二区视频在线| 亚洲av中文字字幕乱码综合| 亚洲av在线观看美女高潮| 久久国内精品自在自线图片| 久久久精品94久久精品| 欧美少妇被猛烈插入视频| 亚洲av一区综合| 免费在线观看成人毛片| 国产久久久一区二区三区| 免费看av在线观看网站| 日本一二三区视频观看| 欧美另类一区| 国内揄拍国产精品人妻在线| 亚洲av成人精品一二三区| a级毛片免费高清观看在线播放| 七月丁香在线播放| 全区人妻精品视频| 国产欧美亚洲国产| 国产成人freesex在线| 99精国产麻豆久久婷婷| 99热国产这里只有精品6| 成人国产麻豆网| 91久久精品电影网| 国产高清不卡午夜福利| 欧美激情久久久久久爽电影| 日日摸夜夜添夜夜添av毛片| 99视频精品全部免费 在线| 亚洲精品色激情综合| 精品人妻熟女av久视频| 五月开心婷婷网| 国产精品偷伦视频观看了| 日日撸夜夜添| 九九久久精品国产亚洲av麻豆| 边亲边吃奶的免费视频| 国产成人精品福利久久| 国产一区二区三区综合在线观看 | 国产日韩欧美亚洲二区| 禁无遮挡网站| 亚洲精品国产成人久久av| 啦啦啦在线观看免费高清www| 免费电影在线观看免费观看| 国产av国产精品国产| 免费看日本二区| 真实男女啪啪啪动态图| 看免费成人av毛片| 边亲边吃奶的免费视频| 97人妻精品一区二区三区麻豆| 欧美xxⅹ黑人| av福利片在线观看| 国产精品一区二区在线观看99| 午夜福利在线观看免费完整高清在| 精品国产露脸久久av麻豆| 26uuu在线亚洲综合色| 美女cb高潮喷水在线观看| 成人午夜精彩视频在线观看| 国产av码专区亚洲av| 国内精品美女久久久久久| 人妻一区二区av| 美女被艹到高潮喷水动态| 精品酒店卫生间| 男女边摸边吃奶| 91aial.com中文字幕在线观看| 免费大片18禁| 久久久久性生活片| 亚洲成人一二三区av| 亚洲av免费高清在线观看| 一本久久精品| 日本熟妇午夜| 中文字幕av成人在线电影| 国产精品一及| 久久精品综合一区二区三区| 男插女下体视频免费在线播放| 小蜜桃在线观看免费完整版高清| 精品一区二区免费观看| 久久久色成人| 中文字幕亚洲精品专区| 水蜜桃什么品种好| 精品久久久精品久久久| 国产在视频线精品| 国产精品国产三级国产专区5o| 麻豆精品久久久久久蜜桃| 成人午夜精彩视频在线观看| 高清日韩中文字幕在线| 亚洲婷婷狠狠爱综合网| 99热全是精品| 欧美日韩国产mv在线观看视频 | 七月丁香在线播放| 爱豆传媒免费全集在线观看| 日日摸夜夜添夜夜爱| 亚洲成人一二三区av| 人妻 亚洲 视频| 午夜精品一区二区三区免费看| 国产高清三级在线| 黄色欧美视频在线观看| 尾随美女入室| 日韩欧美 国产精品| xxx大片免费视频| 亚洲欧美清纯卡通| 欧美3d第一页| 精品久久久精品久久久| av在线播放精品| 最后的刺客免费高清国语| 春色校园在线视频观看| 韩国高清视频一区二区三区| 国产熟女欧美一区二区| 日日摸夜夜添夜夜爱| 国产淫语在线视频| www.色视频.com| 三级国产精品片| 九九在线视频观看精品| 91久久精品国产一区二区成人| 99久久人妻综合| av在线亚洲专区| 欧美日韩综合久久久久久| 永久免费av网站大全| 亚洲精品视频女| 日韩一本色道免费dvd| 久久热精品热| 亚洲自偷自拍三级| 免费看av在线观看网站| 亚洲一区二区三区欧美精品 | 中国美白少妇内射xxxbb| 国内少妇人妻偷人精品xxx网站| 色网站视频免费| h日本视频在线播放| 色5月婷婷丁香| 日韩,欧美,国产一区二区三区| 美女脱内裤让男人舔精品视频| 免费播放大片免费观看视频在线观看| 97精品久久久久久久久久精品| 久久女婷五月综合色啪小说 | 免费看日本二区| 联通29元200g的流量卡| 在线观看人妻少妇| 日韩电影二区| 欧美+日韩+精品| 永久免费av网站大全| 免费播放大片免费观看视频在线观看| 好男人视频免费观看在线| 看黄色毛片网站| 亚洲伊人久久精品综合| 国产91av在线免费观看| 2021天堂中文幕一二区在线观| 王馨瑶露胸无遮挡在线观看| 国产色爽女视频免费观看| 久久久久久久久久成人| 午夜视频国产福利| 另类亚洲欧美激情| 国产精品.久久久| 在线观看av片永久免费下载| 亚洲内射少妇av| 成人亚洲精品av一区二区| 特级一级黄色大片| 全区人妻精品视频| 在线天堂最新版资源| 精品熟女少妇av免费看| 亚洲伊人久久精品综合| 自拍欧美九色日韩亚洲蝌蚪91 | 久久亚洲国产成人精品v| 国产乱人视频| 国内揄拍国产精品人妻在线| 国产伦精品一区二区三区视频9| 国国产精品蜜臀av免费| 欧美日韩国产mv在线观看视频 | 久久久色成人| 中文在线观看免费www的网站| 大香蕉久久网| 国产探花极品一区二区| 一个人看的www免费观看视频| 男女边摸边吃奶| 日本猛色少妇xxxxx猛交久久| 国产成人a∨麻豆精品| 女的被弄到高潮叫床怎么办| 神马国产精品三级电影在线观看| 亚洲欧美一区二区三区国产| 三级国产精品欧美在线观看| 最近最新中文字幕免费大全7| 女人十人毛片免费观看3o分钟| 久久97久久精品| 91在线精品国自产拍蜜月| 国产中年淑女户外野战色| 亚洲在线观看片| 成年女人在线观看亚洲视频 | 一区二区av电影网| 爱豆传媒免费全集在线观看| 久久人人爽av亚洲精品天堂 | 一级毛片久久久久久久久女| 国产黄色视频一区二区在线观看| 亚洲人成网站高清观看| 久久久久久久久久成人| 大香蕉久久网| 亚洲精品aⅴ在线观看| 七月丁香在线播放| 久久综合国产亚洲精品| 国产精品伦人一区二区| 国产精品一区二区性色av| 九色成人免费人妻av| 插阴视频在线观看视频| av女优亚洲男人天堂| 人妻系列 视频| 亚洲图色成人| 国产精品不卡视频一区二区| 91久久精品电影网| 亚洲最大成人av| 亚洲久久久久久中文字幕| 国产伦在线观看视频一区| 成人高潮视频无遮挡免费网站| 永久免费av网站大全| 欧美日韩在线观看h| 国产一区二区亚洲精品在线观看| 在线观看美女被高潮喷水网站| 国产中年淑女户外野战色| 伦精品一区二区三区| 亚洲精品aⅴ在线观看| 日产精品乱码卡一卡2卡三| 亚洲天堂av无毛| 91久久精品国产一区二区三区| 国产亚洲精品久久久com| 熟妇人妻不卡中文字幕| 中文天堂在线官网| 午夜免费鲁丝| 欧美成人精品欧美一级黄| 国产日韩欧美亚洲二区| 日韩成人伦理影院| 国产av码专区亚洲av| 欧美日韩视频精品一区| 欧美精品国产亚洲| 国产老妇伦熟女老妇高清| 亚洲性久久影院| 国产 一区精品| 精品一区二区免费观看| 男女国产视频网站| 性插视频无遮挡在线免费观看| 免费看日本二区| 人妻制服诱惑在线中文字幕| 亚州av有码| 欧美xxxx性猛交bbbb| 亚洲丝袜综合中文字幕| 人体艺术视频欧美日本| 最新中文字幕久久久久| av在线天堂中文字幕| 日韩免费高清中文字幕av| 熟妇人妻不卡中文字幕| av在线观看视频网站免费| 国产中年淑女户外野战色| 亚洲国产日韩一区二区| 日韩av在线免费看完整版不卡| 亚洲av成人精品一二三区| 国产一区二区亚洲精品在线观看| 一本一本综合久久| 成人综合一区亚洲| 成年女人看的毛片在线观看| 一级a做视频免费观看| 国产欧美日韩一区二区三区在线 | 成年av动漫网址| 欧美97在线视频| 只有这里有精品99| 午夜亚洲福利在线播放| 国产一级毛片在线| 黄色日韩在线| videossex国产| .国产精品久久| 一区二区三区免费毛片| 肉色欧美久久久久久久蜜桃 | 久久精品国产鲁丝片午夜精品| 国产精品一区二区三区四区免费观看| 日韩欧美精品v在线| 搡老乐熟女国产| 国产v大片淫在线免费观看| 日韩国内少妇激情av| 99热国产这里只有精品6| 丰满少妇做爰视频| 日韩,欧美,国产一区二区三区| 爱豆传媒免费全集在线观看| 一二三四中文在线观看免费高清| 久久久久国产精品人妻一区二区| 亚洲人成网站高清观看| 大陆偷拍与自拍| 久久久久久久久大av| 精品午夜福利在线看| 深爱激情五月婷婷| 亚洲不卡免费看| 国产精品一区二区性色av| 一级毛片久久久久久久久女| 美女视频免费永久观看网站| 亚洲欧美日韩东京热| 日本色播在线视频| 黄色怎么调成土黄色| 又爽又黄a免费视频| 国产极品天堂在线| 少妇裸体淫交视频免费看高清| 国产一级毛片在线| 在线看a的网站| 少妇猛男粗大的猛烈进出视频 | h日本视频在线播放| 五月伊人婷婷丁香| 成人漫画全彩无遮挡| 国产 精品1| 黄色一级大片看看| 七月丁香在线播放| 亚洲精品日本国产第一区| 国产精品99久久久久久久久| 97人妻精品一区二区三区麻豆| 久久影院123| 久热久热在线精品观看| 秋霞在线观看毛片| 91狼人影院| 一级毛片 在线播放| 国产黄色视频一区二区在线观看| 熟女av电影| 2022亚洲国产成人精品| 国产精品国产三级国产专区5o| 亚洲自拍偷在线| 欧美成人精品欧美一级黄| 成人国产麻豆网| 老师上课跳d突然被开到最大视频| 国产一区亚洲一区在线观看| 另类亚洲欧美激情| 91在线精品国自产拍蜜月| 黄色一级大片看看| 国国产精品蜜臀av免费| 少妇 在线观看| 国产又色又爽无遮挡免| 五月伊人婷婷丁香| 欧美激情在线99| 女人被狂操c到高潮| 美女脱内裤让男人舔精品视频| 一本色道久久久久久精品综合| 女人被狂操c到高潮| 22中文网久久字幕| 男人狂女人下面高潮的视频| 少妇人妻久久综合中文| 美女被艹到高潮喷水动态| 欧美性猛交╳xxx乱大交人| 久久鲁丝午夜福利片| 卡戴珊不雅视频在线播放| 色网站视频免费| 性色avwww在线观看| 日本熟妇午夜| 日韩精品有码人妻一区| 一区二区三区乱码不卡18| 国产欧美日韩一区二区三区在线 | 日韩成人伦理影院| 精品一区二区免费观看| 亚洲精品中文字幕在线视频 | 一本色道久久久久久精品综合| 亚洲成人久久爱视频| 人妻 亚洲 视频| 国产精品国产三级专区第一集| 亚洲精品国产成人久久av| 国产真实伦视频高清在线观看| 99久久中文字幕三级久久日本| 成人二区视频| 亚洲激情五月婷婷啪啪| 最后的刺客免费高清国语| 国产老妇女一区| av国产久精品久网站免费入址| 大香蕉久久网| 91在线精品国自产拍蜜月| 欧美变态另类bdsm刘玥| 免费不卡的大黄色大毛片视频在线观看| 久久久久国产精品人妻一区二区| 久久女婷五月综合色啪小说 | 18禁在线无遮挡免费观看视频| 午夜免费观看性视频| 七月丁香在线播放| 欧美高清性xxxxhd video| 国产精品一及| 免费av观看视频| 卡戴珊不雅视频在线播放| 午夜福利在线观看免费完整高清在| 午夜激情福利司机影院| 亚洲欧洲国产日韩| 久热久热在线精品观看| 成人亚洲欧美一区二区av| 啦啦啦在线观看免费高清www| 成人鲁丝片一二三区免费| 亚洲国产精品999| 在线观看一区二区三区| 狂野欧美激情性bbbbbb| 99精国产麻豆久久婷婷| 少妇裸体淫交视频免费看高清| 另类亚洲欧美激情| 国产精品女同一区二区软件| 一个人看的www免费观看视频| 久久久久网色| 亚洲av欧美aⅴ国产| 在现免费观看毛片| 色综合色国产| 听说在线观看完整版免费高清| av在线老鸭窝| 国产精品成人在线| 丝袜美腿在线中文| 小蜜桃在线观看免费完整版高清| 亚洲国产精品专区欧美| 中文字幕亚洲精品专区| av国产免费在线观看| 精品99又大又爽又粗少妇毛片| 亚洲精品成人久久久久久| 最后的刺客免费高清国语| av在线亚洲专区| 中文字幕av成人在线电影| 18禁裸乳无遮挡动漫免费视频 | 在线天堂最新版资源| 国产精品久久久久久av不卡| 久久久久久久久久久免费av| 日本av手机在线免费观看| 特级一级黄色大片| 一区二区三区四区激情视频| 在线看a的网站| 久热久热在线精品观看| 七月丁香在线播放| 最近最新中文字幕大全电影3| 免费电影在线观看免费观看| 国产精品久久久久久久久免| 亚洲精品自拍成人| 最近手机中文字幕大全| 伦理电影大哥的女人| 听说在线观看完整版免费高清| 亚洲三级黄色毛片| 亚洲精品乱久久久久久| videos熟女内射| 日韩欧美精品v在线| 国产美女午夜福利| 国产免费一级a男人的天堂| 一级毛片久久久久久久久女| 青春草视频在线免费观看| 国产黄a三级三级三级人| 在线精品无人区一区二区三 | 国产爽快片一区二区三区| 亚洲伊人久久精品综合| 亚洲美女搞黄在线观看| 亚洲天堂av无毛| 亚洲成人久久爱视频| 久久久久久九九精品二区国产| 国产精品嫩草影院av在线观看| 亚洲av二区三区四区| 一级二级三级毛片免费看| 国产伦精品一区二区三区四那| 黑人高潮一二区| videos熟女内射| 国产精品一及| 新久久久久国产一级毛片| 97在线视频观看| 夫妻性生交免费视频一级片| a级毛色黄片| 爱豆传媒免费全集在线观看| 男女无遮挡免费网站观看| 一级毛片电影观看| a级一级毛片免费在线观看| 国产亚洲午夜精品一区二区久久 | 亚洲自拍偷在线| 干丝袜人妻中文字幕| 天天躁夜夜躁狠狠久久av| 亚洲欧美一区二区三区国产| 22中文网久久字幕| 午夜爱爱视频在线播放| 国产永久视频网站| 狂野欧美白嫩少妇大欣赏| 精品久久久久久电影网| 国产伦理片在线播放av一区| 国产老妇女一区| 成年免费大片在线观看| 亚洲av福利一区| 色综合色国产| 秋霞在线观看毛片| 亚洲av免费在线观看| 免费大片黄手机在线观看| 黄色视频在线播放观看不卡| 少妇人妻久久综合中文| 色5月婷婷丁香| 五月天丁香电影| 精品国产三级普通话版| 91精品一卡2卡3卡4卡| 久久6这里有精品| 国产老妇女一区| 麻豆成人午夜福利视频| av网站免费在线观看视频| 精品人妻熟女av久视频| 2022亚洲国产成人精品| 成年免费大片在线观看| 精品国产乱码久久久久久小说| 国产精品久久久久久久久免| 少妇丰满av| 亚洲av在线观看美女高潮| av免费在线看不卡| 一级爰片在线观看| 欧美国产精品一级二级三级 | 伊人久久国产一区二区| 少妇裸体淫交视频免费看高清| 亚洲精品aⅴ在线观看| 久久精品久久久久久久性| 精品国产三级普通话版| 日本一本二区三区精品| 亚洲av福利一区| 美女被艹到高潮喷水动态| 国产精品爽爽va在线观看网站| 亚洲国产精品999| 一区二区三区四区激情视频| 国产精品一区二区在线观看99| 最后的刺客免费高清国语| 国产精品一区二区性色av| 国产中年淑女户外野战色| 日韩欧美精品v在线| 99久久人妻综合| 国产国拍精品亚洲av在线观看| 日韩成人伦理影院|