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no-repeat 6px;background-position:center;margin-top:10px;min-width:55px;display:none;} @media (min-width:768px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .expand_btn{display:none;}} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{-webkit-flex:9;-ms-flex:9;flex:9;text-align:left;padding:21px 37px 10px 0px;font-size:16px;font-weight:700;} @media (min-width:768px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 18px 10px 18px;}} @media (min-width:992px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 21px 10px 21px;}} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{-webkit-flex:1;-ms-flex:1;flex:1;background:url('data:image/svg+xml;base64,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') no-repeat 6px;background-position:center;padding:16px 0 16px 0;min-width:62px;max-width:62px;} @media (min-width:768px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:16px 0 17px 0;min-width:52px;max-width:52px;}} @media (min-width:992px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:15px 0 17px 0;min-width:60px;max-width:60px;}} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url:hover{-webkit-text-decoration:none;text-decoration:none;} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption-text{font-size:14px;padding:0px 18px 17px 22px;} @media (min-width:768px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 15px 15px 18px;}} @media (min-width:992px){.kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 18px 19px 21px;}} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption-text figcaption{margin:0;padding:0;} .kbsBFf .floats-partial-section .floats-partial-footer .floats-partial-caption-text svg{max-width:100%;} .kbsBFf ol{font-size:16px;padding-left:32px;font-family:STIXGeneral-Regular;line-height:24px;margin:15px 0;} .kbsBFf ol li{margin:10px 0;padding-left:10px;word-break:normal;} .kbsBFf ol li a{word-break:break-all;} .kbsBFf ol li .reflinks{display:block;} .kbsBFf ol li .reflinks a{color:#4D8A17;-webkit-text-decoration:none;text-decoration:none;cursor:pointer;} .kbsBFf ol li .reflinks a:hover{-webkit-text-decoration:underline;text-decoration:underline;} .kbsBFf ol li .reflinks .sep{color:#000000;} .kbsBFf .table-wrapper{width:auto;overflow-x:auto;} .kbsBFf .table-wrapper::-webkit-scrollbar{-webkit-appearance:none;width:5px;height:5px;} .kbsBFf .table-wrapper::-webkit-scrollbar-thumb{border-radius:5px;background-color:rgba(0,0,0,.5);-webkit-box-shadow:0 0 1px rgba(255,255,255,.5);} @media (min-width:768px){.kbsBFf .table-wrapper{overflow-x:auto;width:auto;-webkit-break-word:word-break;break-word:word-break;margin:0 auto;}} @media (min-width:992px){.kbsBFf .table-wrapper{width:auto;}} @media (min-width:1324px){.kbsBFf .table-wrapper{width:auto;}} @media (min-width:320px){.kbsBFf{margin-bottom:33px;padding:0;}} @media (min-width:992px){.kbsBFf{padding:0;}} @media (min-width:1325px){} .kbsBFf .owl-stage{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;} .kbsBFf .floats-partial-middle--thumbs{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;} .kbsBFf .floats-partial-middle--thumbs .partial-carousel-dot svg{fill:#4D8A17;} .kbsBFf .floats-partial-middle--thumbs .partial-carousel-dot strong{color:#4D8A17;font-weight:500;} .kbsBFf .floats-partial-middle--thumbs .partial-carousel-dot.active strong{color:#007B8B;} .kbsBFf .floats-partial-middle--thumbs .partial-carousel-dot.active svg{fill:#007B8B;} .kbsBFf .floats-partial-middle--thumbs .partial-carousel-dot .nowrap{white-space:nowrap;} .kbsBFf .partial-carousel-dot{text-align:center;cursor:pointer;border:0;width:100px;padding-bottom:20px;background-color:#fff;display:table-cell;} .kbsBFf .partial-carousel-dot:focus-visible{outline-offset:-3px;} .kbsBFf .partial-carousel-dot strong{font-size:11px;padding-top:12px;} @media (min-width:767.5px){.kbsBFf .partial-carousel-dot strong{font-size:16px;}} .kbsBFf .partial-carousel-dot img{width:90px;height:90px;border:1px solid #979797;margin:10px 0;} .kbsBFf .partial-carousel-wrapper .floats-partial-middle{border-top:1px solid #e3e3e3;display:block;margin-bottom:50px;padding-top:1px;} @media (min-width:767.5px){.kbsBFf .partial-carousel-wrapper .floats-partial-middle{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;margin-bottom:0;}} .kbsBFf .partial-carousel--single-image{overflow:hidden;text-align:center;} .kbsBFf 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class="threeColumn__Content-sc-1bf4078-4 jknsZc content"> <div class="articleContent__ArticleContentsWrapper-sc-2yl9jy-0 eSvjPB article_contents"> <div class="articleContent__ArticleContentsSectionsWrapper-sc-2yl9jy-1 jntdRu article_contents__sections_wrapper"> <div class="articleContent__ArticleWrapper-sc-2yl9jy-2 hAzjHP"> <div class="sc-chbbiW mBGSe article_header_class"> <div class="sc-kxynE fgGykB"> <span class="sc-cooIXK gQxyld">特殊的问题</span> <h2 class="sc-fcdeBU bVtmCk">代理的安全措施在交通安全分析</h2> <a class="sc-htpNat bUhGXt link sc-gmeYpB hSHJbi" href="//www.newsama.com/journals/jat/si/987045/" aria-label="">把这个特殊的问题</a> <hr class="sc-clNaTc ktFfKO"> </div> <p class="sc-kZmsYB bkiEfx article_type">研究文章|开放获取</p> <div class="sc-RcBXQ ztvYg article_citation"> 体积 2021年 <span class="sc-fZwumE cUsFHx">|</span>文章的ID 5569143 <span class="sc-fZwumE cUsFHx">|</span> <a class="sc-htpNat bUhGXt link sc-fQejPQ duuwge" href="https://doi.org/10.1155/2021/5569143" aria-label="Doi-link">https://doi.org/10.1155/2021/5569143</a> </div> <div class="sc-LKuAh lcOiDF article_full_citation"> <div class="sc-hzNEM gsmOCv isHide"> <p>剑,西安Liu称,小池, ”<span class="adjust-article-svg-size">动态贝叶斯网络实时城市高架高速公路事故预测模型</span>”,<i>《先进的交通工具</我>, 卷。2021年, 文章的ID5569143, 12   页面, 2021年。 https://doi.org/10.1155/2021/5569143</p> </div> <button class="sc-hSdWYo etauus sc-iBEsjs kPshFw" color="">显示引用</button> </div> <h1 class="sc-iRbamj jZOxOO sc-iSDuPN cnKfwa article_title adjust-article-svg-size heading-title">动态贝叶斯网络实时城市高架高速公路事故预测模型</h1> </div> <div class="sc-jrIrqw eHMFfo article_authors"> <hr class="sc-cEvuZC XkrFe hr__author"> <span class="">西安刘,<sup>1,2,3</sup></span> <span class=""><b>剑路</b><a class="sc-htpNat bUhGXt link" href="mailto:" aria-label="Mail Option"><span class="sc-htoDjs kNoiZu sc-hjRWVT irNWJI icon-colored _2zt-1j0LmaF8UjTQAM413G icon-themeable" title="" font-size="16"></span></a><a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0002-9661-1337" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>,<sup>1,2,3</sup></span> <span class="">称程,<sup>1,2,3</sup> 和</span> <span class="">记得马<sup>1,2,3</sup></span> <div class="sc-fHxwqH dMuGGA isHide"> <p><sup>1</sup><span>江苏城市的重点实验室,东南大学,南京211189年,中国</span></p> <p><sup>2</sup><span>江苏省现代城市交通技术的协同创新中心,东南大学,南京211189年,中国</span></p> <p><sup>3</sup><span>211189年东南大学交通学院、南京,中国</span></p> </div> <button class="sc-hSdWYo etauus sc-iQtOjA kfDwZY" color="">显示更多</button> </div> <div class="articleContent__EditorWrapper-sc-2yl9jy-3 evMkvU"> <div class="articleContent__TextWrapper-sc-2yl9jy-8 fYrLtk"> <b>学术编辑器:</b>Eneko Osaba</div> <div class="articleContent__PublicationTimeLineWrapper-sc-2yl9jy-4 fjviok"> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">收到了</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2021年1月24日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">修改后的</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2021年3月08</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">接受</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2021年5月06</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC isPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">发表</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2021年5月15</span> </div> </div> </div> <article class="sc-jtRlXQ kbsBFf article_body "> <div> <div class="xml-content"> <h4 class="header" id="abstract">文摘</h4> <p>交通事故是一个复杂的现象,涉及耦合在多个影响因素之间相互依存。考虑到准确预测崩盘风险的相互依存是至关重要的,有助于揭示事故发生的潜在机制,本研究试图建立一个实时碰撞预测模型(RTCPM)城市高架高速公路占动态性和耦合相互依存在事故发生前交通流特征,确定最可能的风险传播路径和最重要的贡献者崩溃的风险。在这项研究中,动态贝叶斯网络(DBN) RTCPM的框架。随机森林(RF)法来识别最重要的变量,这是用于构建DBN-based RTCPMs。电脑算法结合专家经验进一步应用于调查之间的耦合相互依存DBN模型中的交通流特征。比较分析在改善DBN-based RTCPM考虑到相互依存,原DBN-based RTCPM没有考虑到互相依赖,和多层感知器(MLP)。此外,灵敏度和强度影响的分析是用来确定最可能的风险传播路径和最重要的贡献者崩溃的风险。结果表明,改进的DBN-based RTCPM有更好的预测性能比原来DBN-based RTCPM RTCPM和延时。最可能的风险影响路径被确认如下:当前段速度(V)(时间片2)⟶V(时间片1)⟶速度对上游段(U_V)(时间片1)⟶流量性能指标(TPI)(时间片1)⟶崩盘风险。最敏感因素事故风险在这个路径是V(时间片2),其次是TPI(时间片1),V(时间片1),U_V(时间片1)。这些结果表明,改进的DBN-based RTCPM有潜力预测城市高架高速公路实时崩溃。 Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.</p> <span class="end-abs"></span> <h4 id="introduction">1。介绍</h4> <p>实时道路交通事故预测是道路安全的热点的背景下主动交通管理(ATM)在过去的二十年里。实时碰撞预测是指假设一个特定的路上发生车祸的概率部分可以在很短的预碰撞预测时间间隔,采用瞬时交通流特征(<a href="#B1">1</a>- - - - - -<a href="#B3">3</a>]。智能交通系统(ITS)的发展和先进的交通信息系统(ATIS航站)有助于容易收集实时交通数据,促进有效和准确评估碰撞风险在高速公路和高速公路利用RTCPMs [<a href="#B4">4</a>- - - - - -<a href="#B11">11</a>]。</p> <p>一般来说,许多RTCPMs研究交通流数据之间建立直接连接(即。、体积、速度、占用及其组合)和事故数据。在这些模型中,共线性和避免因变量之间的相关性;因此,变量的独立性是保证<a href="#B12">12</a>,<a href="#B13">13</a>]。然而,交通事故是一个复杂的现象,包括耦合在多个影响因素之间相互依存。耦合相互依存的概念可以用来表达各种风险之间的相互作用。虽然这些复杂的系统因素可以表现出很多自己的特点,在现实中这些个人因素互动和夫妇以更复杂的方式相互耦合的方向和耦合强度(<a href="#B14">14</a>- - - - - -<a href="#B16">16</a>]。这种交互称为耦合相互依存,从而导致增加或减少事故的风险(<a href="#B17">17</a>]。因此,它对RTCPMs占至关重要影响因素之间的相关性。此外,交通数据是经常采用的一次性间隔实时碰撞预测的RTCPMs [<a href="#B6">6</a>,<a href="#B18">18</a>]。然而,对于城市高架高速公路、合并和换道行驶行为经常由于dense-ramp设置。交通流的特点是容易显示动态性变化随着时间的推移,这是与事故风险密切相关(<a href="#B19">19</a>]。因此,动态性的交通流时间维度应该考虑实现的RTCPM预测城市高架高速公路事故。</p> <p>DBN,特定形式的贝叶斯网络(BN),代表了某种状态空间模型的动态演化在时间(<a href="#B20">20.</a>]。它被广泛用于预测和评估风险的动态演变过程领域的海上事故,隧道施工,ship-ice碰撞等。<a href="#B21">21</a>- - - - - -<a href="#B23">23</a>]。为了表达交通流的动态性特点,一些RTCPMs研究应用时间序列组成的交通数据几位时间间隔<a href="#B24">24</a>- - - - - -<a href="#B27">27</a>),被证明是可行的和鲁棒性。然而,这些研究忽略相关性的调查在不同交通流特征和简单连接每个影响因素直接崩溃风险图结构的施工过程。DBN建设的最关键的一步,互相依赖的变量可以评估DBN模型与应用程序的结构学习算法。此外,神经网络模型(例如,MLP)也能够适应相关的因变量。然而,整个模型应该重建并重新调整一次未来新的变量和知识从数据输入,其优化过程可以高度resource-demanding [<a href="#B13">13</a>]。</p> <p>此外,考虑影响因素之间的相关性也有助于揭示事故发生的潜在机制。目前的研究估计,事故风险量化事故发生的概率。我们希望这个模型可以实时提供一些对策协调风险很高的时候,碰撞的概率。对策的制定应基于风险传播路径的识别和重大风险因素。然而,有预测和解释模型之间的两难境地:预测模型的专业不是最好的知识发现,反之亦然(<a href="#B7">7</a>,<a href="#B28">28</a>]。DBN模型的优势实现不确定性分析和概率推理和不确定性进行双向调查预测和诊断分析。结合灵敏度和强度影响的分析方法不仅可以确定最可能的风险传播路径,但也可以认识到最敏感的贡献者在传播路径(<a href="#B29">29日</a>]。一旦最敏感的风险因素在整个传播路径显示,引用的顺序和重点提供中介,这有助于制定相应的实时风险对策切断传播路径,降低事故发生的概率。</p> <p>现有DBN-based RTCPMs主要强调交通流的动态性特点,缺乏调查交通流之间的相互依存的耦合特性。本研究的主要贡献是(1)DBN结构学习算法应用于一个例子来预测道路交通事故;(2)比较两个DBN-based的表演RTCPMs(考虑到相互依赖和不考虑相互依存)和MLP-based RTCPM;和(3)确定最敏感的风险因素在传播路径的使用影响分析的灵敏度和强度。</p> <p>手稿分为五部分。其余部分组织如下。节<a href="#sec2">2</a>,并给出了材料和方法。部分<a href="#sec3">3</a>提出和讨论的结果。部分<a href="#sec4">4</a>提供了一些结论。</p> <h4 id="materials-and-methods">2。材料和方法</h4> <h5 id="sec2.1">2.1。研究区和数据准备</h5> <p>40的延安高架高速公路在上海,中国,沿着西行的顺序互相连接起来,往东的高速公路,被选为研究区域(见图<a href="//www.newsama.com/journals/jat/2021/5569143/fig1/" target="_blank">1</a>)。所有部分都是三车道与探测器间距近似300 - 500米的距离。每个部分都有类似的道路几何和/出站安排;因此,道路几何图形和坡道的位置并不视为影响变量在崩溃的风险。有82事故发生在延安高速公路在2018年8月和9月。的日期、时间和段收集IDs的崩溃。基于匹配的病例对照设计,三个相应noncrash情况下为每个事故情况下随机匹配相同的部分和发生时间(总共246 noncrash病例)。此外,交通流特征和天气RTCPM也获得作为输入变量,针对事故分类和noncrash国家基于崩溃风险之间的关系的调查,交通流特征和天气条件。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig1"> <div class="floats-partial-content fig-content-fig1"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.001" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.001.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 延安高架高速公路,上海,中国。</div> </div> </div> <p>现有dual-loop探测器可用于研究领域提供了平均速度(公里/小时)和单一的平均体积巷(pcu / h)为每一个段。每小时天气变量,包括能见度(公里)和天气类型)(雨天还是晴天,从上海徐家汇天文台收集,从延安高速公路7.5公里远。在这项研究中,交通性能指标(TPI)不同在0和1之间应用作为一个指标来衡量拥堵程度的大小,1是一个交通堵塞状态,0是一个自由流动状态(方程(<a href="#EEq1">1</a>))。考虑<span class="equation_break" id="EEq1"><span class="left_break" id="L1"></span><span class="middle_break"><span class="center_break" id="M1"> <svg height="32.6138pt" id="M1-1" version="1.1" viewbox="0 0 97.924 32.6138" width="97.924pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B1-1"> <g transform="matrix(.013,0,0,-0.013,0,20.093)"> <path d="M592 498C586 556 582 629 581 675H560C545 656 537 650 510 650H115C87 650 74 652 60 675H40C38 620 33 555 28 495H57C69 539 77 568 89 585C102 607 122 616 205 616H267V123C267 44 259 34 168 28V0H454V28C360 34 352 44 352 123V616H422C498 616 514 608 531 584C542 568 552 543 562 495L592 498Z" id="g190-85"></path> </g> <g transform="matrix(.013,0,0,-0.013,8.02,20.093)"> <path d="M46 650V622C120 617 128 613 128 525V125C128 42 120 34 40 28V0H311V28C221 34 212 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<g transform="matrix(.0091,0,0,-0.0091,71.82,32.45)"> <use xlink:href="#g58-118"></use> </g> <g transform="matrix(.013,0,0,-0.013,94.96,20.093)"> <path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path> </g> </g> </svg></span></span><span class="right_break" id="R1"> <svg class="label" height="11.44pt" id="M1-2" version="1.1" viewbox="0 0 13.398 11.44" width="13.398pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B1-2"> <g transform="matrix(.013,0,0,-0.013,0,9.25)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 611 70 451 70 272V271C70 90 136 -71 282 -169L300 -147Z" id="g190-41"></path> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.25)"> <path d="M311 0V25C221 32 216 41 216 113V635C162 613 103 594 39 583V559L88 556C131 552 135 546 135 500V113C135 41 131 32 40 25V0H311Z" id="g190-213"></path> </g> <g transform="matrix(.013,0,0,-0.013,8.9,9.25)"> <path d="M275 271C275 451 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 269V271Z" id="g190-42"></path> </g> </g> </svg></span></span>在哪里<我>V</我><sub>马克斯</sub>最大速度和吗<我>V</我><sub><i>我</我></sub>平均速度吗<我>我</我>时间。</p> <p>对当前的平均速度数据,上游和下游的碰撞位置和整个高速公路的TPI聚合在5分钟的间隔。交通流随时间的演变导致崩溃是一个动态过程;因此,交通流特征的几个时间间隔在坠机前应结合建立模型。0 - 5分钟的时间间隔(时间片0),5 - 10分钟(时间片1),10 - 15分钟(时间片2)崩溃之前被认为是。时间片0被排除在外,因为碰撞预警系统需要一些时间认识到崩溃,和实际事故发生时间和记录时间并不总是完全一致的。由于原始气象数据更新一次一个小时,天气条件被认为是一个稳定的影响变量在不同的时间片。最后,交通流量和天气数据对应于82年崩盘病例和246 noncrash例生成。总共9个变量结合当前交通流特点,上游和下游的碰撞位置与天气条件如表所示<a href="//www.newsama.com/journals/jat/2021/5569143/tab1/" target="_blank">1</a>。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab1"> <div class="floats-partial-content fig-content-tab1"> <table class="table-group" id="tab1"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="2"> <hr></td> </tr> <tr class="thead"> <td class="align_left">变量</td> <td class="align_center">描述</td> </tr> <tr> <td class="thead-hr" colspan="2"> <hr></td> </tr> <tr> <td class="align_left">TPI</td> <td class="align_center">的平均TPI整个高速公路5分钟间隔内</td> </tr> <tr> <td class="align_left">V</td> <td class="align_center">当前段5分钟间隔内的平均速度(公里/小时)</td> </tr> <tr> <td class="align_left">U_V</td> <td class="align_center">上游段5分钟间隔内的平均速度(公里/小时)</td> </tr> <tr> <td class="align_left">D_V</td> <td class="align_center">下游段5分钟间隔内的平均速度(公里/小时)</td> </tr> <tr> <td class="align_left">问</td> <td class="align_center">当前段5分钟间隔内的体积(pcu /小时)</td> </tr> <tr> <td class="align_left">U_Q</td> <td class="align_center">上游段5分钟间隔内的体积(pcu /小时)</td> </tr> <tr> <td class="align_left">D_Q</td> <td class="align_center">下游段5分钟间隔内的体积(pcu /小时)</td> </tr> <tr> <td class="align_left">可见性</td> <td class="align_center">在一小时内水平能见度(公里)</td> </tr> <tr> <td class="align_left">天气</td> <td class="align_center">天气类型在一个小时内,雨天或晴天</td> </tr> <tr class="table-tr"> <td colspan="2"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/tab1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 信息的替代变量。</div> </div> </div> <h5 id="sec2.2">2.2。随机森林(RF)</h5> <p>构建RTCPM的主要目的是评估碰撞风险。高维变量空间可以增加处理RTCPM的复杂性。因此,随机森林(RF),一种广泛使用的变量选择模型,实现在这项研究中,选择影响变量和减少冗余的变量。变量重要性(VI)指标作为标准来选择相关的主要变量(<a href="#B12">12</a>,<a href="#B30">30.</a>),可以确定以下步骤。<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">样本<我>N</我>从学习的数据量将建立一个树分类器通过引导样品技术,和剩下的学习样本集不习惯在这棵树的生长。忽略时样品,一个有效的内部测试数据集,被称为out-of-bag (OOB)数据,得到一个公正的错误估计了。<我>米</我>随机选择的变量数量从原来的变量集<我>米</我>(<我>米</我><<我>米</我>),最好的分离变量<我>米</我>在每个树节点采用分裂节点。每棵树的成长自然没有修剪。重复这个步骤<我>k</我>次构造组成的射频<我>k</我>树。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">每棵树分类器的分类结果由投票产生二进制目标(崩溃或noncrash)基于OOB样本,和分类错误率<我>R</我><sub><i>我</我></sub>计算结果。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">添加随机噪声干扰的任何变量的值OOB样本,和新OOB样本。每棵树,实现事故/ noncrash分类测试与新OOB样本用于计算分类错误率<我>R</我><sub><i>我</我></sub><i>”</我>。</span></span><span class="list-item"><span class="list-label">(4)</span><span class="list-content">VI是计算分类错误率的增加意味着添加随机噪声干扰后的树木。计算公式如下方程:<span class="equation_break" id="EEq2"><span class="left_break" id="L2"></span><span class="middle_break"><span class="center_break" id="M2"> <svg height="37.9605pt" id="M2-1" version="1.1" viewbox="0 0 106.144 37.9605" width="106.144pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B2-1"> <g transform="matrix(.013,0,0,-0.013,0,22.717)"> <path d="M687 650H462V622C543 612 549 605 530 547C498 447 422 252 372 126H370C302 298 229 492 204 563C188 607 191 615 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</svg></span></span></span></span></span></p> <h5 id="sec2.3">2.3。动态贝叶斯网络(DBN)</h5> <p>贝叶斯网络(BN)是一个概率图形化模型表示的概率之间的关系一组变量,这些变量连接在一个有向无环图(DAG)。BN的优势学习因果关系,预测干预的后果,最可能的解释和分析结果。一些研究人员采用BN评估和分析交通事故风险(<a href="#B31">31日</a>- - - - - -<a href="#B33">33</a>]。大多数事故没有发生基于一个特定的时间点,但他们可以通过多个交通状态描述一系列的时间片。DBN是一种BN,两时间序列数据表达风险演变过程随着时间向前流动(<a href="#B20">20.</a>]。概率推理的应用,BN泛化的关键一步是揭示随机变量的概率依赖关系,这也表达了DBN的时间序列。两种类型的依赖关系存在于DBN:依赖一个时间片内和时间片之间的依赖关系。DBN模型由观测得到的证据<我>X</我>= {<我>x</我><sub>1,</sub><i>x</我><sub>2</sub>、…<我>x</我><sub><i>t</我></sub>}和隐藏变量<我>Y</我>= {<我>y</我><sub>1</sub>,<我>y</我><sub>2</sub>、…<我>y</我><sub><i>t</我></sub>},交通状态变量和崩溃的可能性,分别。十亿年当一个马尔可夫模型和集成构建DBN模型中,有一个过渡模式<我>P</我>(<我>x</我><sub><i>t</我></sub>|<我>x</我><sub><i>t−</我>1</sub>),一个观测模型<我>P</我>(<我>y</我><sub><i>t</我></sub>|<我>x</我><sub><i>t</我></sub>),和一个初始状态分布<我>P</我>(<我>x</我><sub>1</sub>)。联合概率分布可以表示如下:<span class="equation_break" id="EEq3"><span class="left_break" id="L3"></span><span class="middle_break"><span class="center_break" id="M3"> 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<path d="M269 381C298 400 325 421 340 436C360 455 370 476 370 504C370 579 313 635 223 635H222C170 635 123 610 95 579L52 516L72 497C95 533 136 577 193 577C245 577 286 546 286 482C286 408 218 369 127 339L133 314C149 319 174 325 197 325C254 325 324 285 324 192C325 83 266 39 201 39C143 39 103 68 78 91C70 98 62 97 54 91C45 84 33 71 32 58C30 46 34 35 48 22C61 10 102 -12 148 -12C216 -12 410 57 410 225C410 298 357 362 269 379V381Z" id="g190-232"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.37,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="79.7249pt" id="IL3" version="1.1" viewbox="0 0 14.93 79.7249" width="14.93pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 33.93)" xlink:href="#B3-3"></use> </svg></span></span></p> <p>有三个关键步骤:初始化一个DBN模型采用(1)ChiMerge算法实现连续变量的离散化。(2)结构学习应用于图形变量之间的依赖关系。在这一步中,DBN不仅估计变量之间的依赖关系在一个时间片,还检查了他们在不同的时间片。电脑算法用于构建BN的结构在一片之间的交通状态变量和崩溃的可能性。然后,同一变量在不同片连接建立DBN的结构。(3)参数学习是学习变量的条件概率分布进行一个时间片内和跨时间片。参数估计是测试的期望最大化(EM)算法。</p> <h5 id="sec2.4">2.4。ChiMerge算法</h5> <p>在DBNs连续变量通常是有问题的,因为它未能捕捉连续变量之间的关系(<a href="#B34">34</a>]。经典方法DBNs处理连续变量是离散化的变量(<a href="#B35">35</a>]。离散化的操作是将连续变量划分为少量的间隔,其中每个区间映射到离散的象征。有两种广泛使用的简单方法,equal-width间隔,这将变量的最小值和最大值之间划分为一个相等的间隔大小、频率相同的间隔,间隔边界在哪里选择基于每个区间包含相同数量的样本。然而,这两个样本的方法忽略了类<a href="#B36">36</a>]。一个好的离散化intrainterval一致性和interinterval区别。通过使用ChiMerge算法执行合并操作<我>χ</我><sup>2</sup>统计测试是否有显著差异或相似之处的相对类频率相邻间隔。</p> <p>ChiMerge算法主要包括几个步骤。<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">样品根据他们的价值。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">计算<我>χ</我><sup>2</sup>值为每一对相邻间隔与以下方程:<span class="equation_break" id="EEq4"><span class="left_break" id="L4"></span><span class="middle_break"><span class="center_break" id="M4"> <svg height="43.916pt" id="M4-1" version="1.1" viewbox="0 0 120.754 43.916" width="120.754pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B4-1"> <g transform="matrix(.013,0,0,-0.013,0,26.434)"> <path d="M565 434L556 448L482 423L322 189L287 373C280 410 261 448 236 448C207 448 161 415 110 338L131 317C160 355 181 374 193 374C204 374 208 366 217 330C230 279 247 185 259 116L50 -175C18 -223 29 -260 41 -278H51C97 -198 207 -33 270 57L313 -165C328 -235 349 -261 376 -261S445 -244 510 -150L487 -127C444 -178 423 -186 411 -186C402 -186 398 -177 387 -130C371 -62 349 39 332 135C390 217 524 392 565 434Z" id="g113-244"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,7.57,20.171)"> <use xlink:href="#g50-51"></use> </g> <g transform="matrix(.013,0,0,-0.013,16.15,26.434)"> <use xlink:href="#g117-34"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,30.32,11.67)"> <path d="M781 91L765 118C733 86 702 70 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transform="matrix(.013,0,0,-0.013,91.51,15.043)"> <path d="M593 650H142L136 622C219 616 224 611 209 531L130 120C114 39 108 34 23 28L17 0H501C518 32 557 130 568 158L540 169C519 132 495 93 469 70C439 43 409 35 332 35C272 35 241 37 225 50C211 61 211 89 222 149L253 320H350C441 320 443 312 441 239H471L510 434H481C455 366 448 358 360 358H260L303 572C311 613 312 615 348 615H431C509 615 522 603 532 581C544 555 546 531 545 491L574 493C583 555 592 631 593 650Z" id="g113-70"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,99.46,18.174)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,101.34,18.174)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,105.77,15.043)"> <use xlink:href="#g119-134"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,111.65,5.85)"> <use xlink:href="#g50-51"></use> </g> <rect height="0.65243" width="60.8664" x="55.73" y="22.781"></rect> <g transform="matrix(.013,0,0,-0.013,79.02,35.66)"> <use xlink:href="#g113-70"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,86.96,38.792)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,88.84,38.792)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,117.79,26.434)"> <use xlink:href="#g113-45"></use> </g> </g> </svg> <svg class="alt_equation" height="43.916pt" id="I4" version="1.1" viewbox="0 0 120.76 43.916" width="120.76pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 0)" xlink:href="#B4-1"></use> </svg></span></span><span class="right_break" id="R4"> <svg class="label" height="11.44pt" id="M4-2" version="1.1" viewbox="0 0 15.458 11.44" width="15.458pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B4-2"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <path d="M465 179V225H369V632H330C227 497 124 346 29 205V179H289V107C289 37 284 33 198 26V0H454V26C374 33 369 37 369 107V179H465ZM289 225H92C156 335 222 430 287 520H289V225Z" id="g190-166"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.96,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="43.916pt" id="IL4" version="1.1" viewbox="0 0 15.52 43.916" width="15.52pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 16.03)" xlink:href="#B4-2"></use> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">在哪里<我>米</我>= 2(2间隔相比较),<我>n</我>= 2(数量的类,即。崩溃和noncrash),<我>一个</我><sub><i>ij</我></sub>=的样本数量<我>我</我>th间隔,<我>j</我>th类,<我>E</我><sub><i>ij</我></sub>=预期的频率<我>一个</我><sub><i>ij</我></sub>。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">最低的两相邻区间合并<我>χ</我><sup>2</sup>值,直到所有成对的间隔<我>χ</我><sup>2</sup>值超出<我>χ</我><sup>2</sup>阈值。的<我>χ</我><sup>2</sup>阈值是由所需的显著性水平(0.95百分比水平研究)和自由度的数量(1小于类的数量)。有两类(崩溃和noncrash);因此,自由度为1。最后,<我>χ</我><sup>2</sup>值是3.841。</span></span></span></p> <h5 id="sec2.5">2.5。计算机算法</h5> <p>个人电脑算法是一种有效的和经典算法用于结构学习BN (<a href="#B37">37</a>,<a href="#B38">38</a>]。电脑算法的过程主要包括三个步骤:<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">确定骨架图的条件独立测试。让<我>X</我>= {<我>x</我><sub>1</sub>,<我>x</我><sub>2</sub>、…<我>x</我><sub><i>k</我></sub>}是一组随机变量和<我>V</我>= {<span class="inline_break"> <svg height="9.10848pt" id="M5" style="vertical-align:-3.291111pt" version="1.1" viewbox="-0.0498162 -5.81737 13.721 9.10848" width="13.721pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M219 86C216 168 211 250 206 337C201 410 189 448 163 448C131 448 79 396 43 344L60 322C91 359 110 375 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height="9.10848pt" style="vertical-align:-3.291111pt" version="1.1" viewbox="31.7511838 -5.81737 18.427 9.10848" width="18.427pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,31.801,0)"> <use xlink:href="#g113-47"></use> </g> <g transform="matrix(.013,0,0,-0.013,36.944,0)"> <use xlink:href="#g113-47"></use> </g> <g transform="matrix(.013,0,0,-0.013,42.087,0)"> <use xlink:href="#g113-47"></use> </g> <g transform="matrix(.013,0,0,-0.013,47.264,0)"> <use xlink:href="#g113-45"></use> </g> </svg><span class="ipunc"></span> <svg height="9.10848pt" style="vertical-align:-3.291111pt" version="1.1" viewbox="52.357183799999994 -5.81737 11.157 9.10848" width="11.157pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,52.407,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,58.218,3.132)"> <use xlink:href="#g50-108"></use> </g> </svg></span>}是一组节点图,这样每个节点中<我>V</我>是一个随机变量<我>X</我>。然后,构造一个无向图<我>G</我>所有节点相互连接,然后电脑算法实现了统计测试来删除或保持相邻节点之间的边<我>x</我><sub><i>我</我></sub>和<我>x</我><sub><i>j</我></sub>给定一个空调<我>x</我><sub><i>γ</我></sub>图中通过计算交叉熵CE (<我>x</我><sub><i>我</我></sub>,<我>x</我><sub><i>j</我></sub>|<我>x</我><sub><i>γ</我></sub>):<span class="equation_break" id="EEq5"><span class="left_break" id="L6"></span><span class="middle_break"><span class="center_break" id="M6"> <svg height="41.0988pt" id="M6-1" version="1.1" viewbox="0 0 72.679 41.0988" width="72.679pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B6-1"> <g transform="matrix(.013,0,0,-0.013,0,23.877)"> <path d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z" id="g190-68"></path> </g> <g 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xlink:href="#g119-133"></use> </g> <g transform="matrix(.013,0,0,-0.013,97.93,35.268)"> <use xlink:href="#g113-121"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,105.13,38.4)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,109.56,35.268)"> <use xlink:href="#g113-9"></use> </g> <g transform="matrix(.013,0,0,-0.013,112.98,35.268)"> <use xlink:href="#g113-121"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,120.18,38.4)"> <use xlink:href="#g50-225"></use> </g> <g transform="matrix(.013,0,0,-0.013,125.4,35.268)"> <use xlink:href="#g119-134"></use> </g> <g transform="matrix(.013,0,0,-0.013,132.49,23.877)"> <use xlink:href="#g113-47"></use> </g> </g> </svg> <svg class="alt_equation" height="106.675pt" id="I6" version="1.1" viewbox="0 0 163.27 106.675" width="163.27pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 -11.39)" xlink:href="#B6-1"></use> <use transform="matrix(1 0 0 1 13.59 15.79)" xlink:href="#B6-2"></use> <use transform="matrix(1 0 0 1 27.79 65.58)" xlink:href="#B6-3"></use> </svg></span></span><span class="right_break" id="R6"> <svg class="label" height="11.44pt" id="M6-4" version="1.1" viewbox="0 0 14.898 11.44" width="14.898pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B6-4"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <path d="M130 550H367L393 615L386 623H101L63 323C82 331 123 342 165 342C275 342 328 277 328 188C328 107 283 40 201 40C141 40 99 72 78 91C69 99 59 98 51 91C39 81 30 67 29 58C28 48 33 37 46 24C60 10 104 -12 149 -12C203 -11 269 14 323 59C384 109 412 165 412 225C412 308 347 399 219 399C193 399 146 382 104 367L130 550Z" id="g190-161"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.4,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="106.675pt" id="IL6" version="1.1" viewbox="0 0 14.96 106.675" width="14.96pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 47.41)" xlink:href="#B6-4"></use> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">个人电脑算法采用<我>G</我><sup>2</sup>检验统计量,等于2<我>n</我>CE<我>(x</我><sub><i>我</我></sub>,<我>x</我><sub><i>j</我></sub><i>| x</我><sub><i>γ</我></sub><i>)</我>与<我>n</我>表明样本大小,来验证独立。第一步的结果是图的骨架。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">搜索<span class="nowrap"> <svg height="6.04987pt" id="M7" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> </svg>- - - - - -</span>结构。如果两个变量<我>x</我><sub><i>我</我></sub>和<我>x</我><sub><i>j</我></sub>不是有条件和给定独立吗<我>x</我><sub><i>γ</我></sub>,然后<svg height="11.3463pt" id="M8" style="vertical-align:-5.52893pt" version="1.1" viewbox="-0.0498162 -5.81737 11.1529 11.3463" width="11.1529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-225"></use> </g> </svg>对撞机节点和一个决定<span class="nowrap"> <svg height="6.04987pt" id="M9" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> </svg>- - - - - -</span>结构<svg height="9.10848pt" id="M10" style="vertical-align:-3.291111pt" version="1.1" viewbox="-0.0498162 -5.81737 8.98808 9.10848" width="8.98808pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-106"></use> </g> </svg>⟶<svg height="11.3463pt" id="M11" style="vertical-align:-5.52893pt" version="1.1" viewbox="-0.0498162 -5.81737 11.1529 11.3463" width="11.1529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-225"></use> </g> </svg>←<svg height="11.3463pt" id="M12" style="vertical-align:-5.52893pt" version="1.1" viewbox="-0.0498162 -5.81737 10.3582 11.3463" width="10.3582pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-107"></use> </g> </svg>画,另一边缘保持无向<svg height="9.10848pt" id="M13" style="vertical-align:-3.291111pt" version="1.1" viewbox="-0.0498162 -5.81737 8.98808 9.10848" width="8.98808pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-106"></use> </g> </svg>−<svg height="11.3463pt" id="M14" style="vertical-align:-5.52893pt" version="1.1" viewbox="-0.0498162 -5.81737 11.1529 11.3463" width="11.1529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-225"></use> </g> </svg>−<span class="nowrap"> <svg height="11.3463pt" id="M15" style="vertical-align:-5.52893pt" version="1.1" viewbox="-0.0498162 -5.81737 10.3582 11.3463" width="10.3582pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"> <use xlink:href="#g50-107"></use> </g> </svg>。</span></span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">确认其他边缘的方向。结合专家经验,一些无向边节点之间基于指定任何周期和其他的原则<span class="nowrap"> <svg height="6.04987pt" id="M16" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g185-40"></use> </g> </svg>- - - - - -</span>配置不允许。</span></span></span></p> <h5 id="sec2.6">2.6。期望最大化(EM)算法</h5> <p>EM算法是一个通用的算法来计算最大可能性日志和性能已经被证明是有效的参数学习BN (<a href="#B39">39</a>]。EM算法的基本理论是学习节点之间的依赖通过迭代过程的参数估计(<a href="#B40">40</a>]。EM算法主要包括三个步骤:<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">初始化<我>θ</我>:给定一组未知参数<我>θ</我>,一个日志可能是最大化的价值。目标函数是<span class="equation_break" id="EEq6"><span class="left_break" id="L17"></span><span class="middle_break"><span class="center_break" id="M17"> <svg height="27.3389pt" id="M17-1" version="1.1" viewbox="0 0 115.428 27.3389" width="115.428pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B17-1"> <g transform="matrix(.013,0,0,-0.013,0,12.205)"> <path d="M442 569C442 628 407 660 362 660C328 660 292 648 255 616C200 568 165 508 123 334L106 264C80 242 52 222 23 202L30 176C54 191 76 205 100 222L87 166C73 103 79 43 95 19C108 0 139 -12 169 -12C242 -12 318 38 369 106L354 129C322 93 260 49 215 49C173 49 147 81 170 187L189 274C293 344 442 452 442 569ZM380 565C380 478 267 377 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<svg class="alt_equation" height="49.4584pt" id="I17" version="1.1" viewbox="0 0 141.67 49.4584" width="141.67pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 -2.92)" xlink:href="#B17-1"></use> <use transform="matrix(1 0 0 1 42.54 22.12)" xlink:href="#B17-2"></use> </svg></span></span><span class="right_break" id="R17"> <svg class="label" height="11.44pt" id="M17-3" version="1.1" viewbox="0 0 15.428 11.44" width="15.428pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B17-3"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.49,9.26)"> <path d="M143 344C173 483 265 547 326 576C363 593 402 605 434 611L429 641C389 634 343 623 301 608C195 570 43 456 43 236C43 81 135 -12 245 -12C368 -12 453 88 453 208C453 310 380 393 273 393C253 393 232 386 211 376L143 344ZM234 338C325 338 367 255 367 172C367 102 346 21 264 21C184 21 132 113 132 239C132 267 133 291 138 312C164 327 197 338 234 338Z" id="g190-227"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.93,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="49.4584pt" id="IL17" version="1.1" viewbox="0 0 15.49 49.4584" width="15.49pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 18.8)" xlink:href="#B17-3"></use> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">介绍一个分布<我>问(Y)</我>:一个初始化的分布<我>θ</我>定义基于詹森不等式:<span class="equation_break" id="EEq7"><span class="left_break" id="L18"></span><span class="middle_break"><span class="center_break" id="M18"> <svg height="32.9303pt" id="M18-1" version="1.1" viewbox="0 0 168.788 32.9303" width="168.788pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B18-1"> <g 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xlink:href="#g119-73"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,75.58,31.445)"> <use xlink:href="#g50-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,85.19,17.796)"> <path d="M699 368C699 549 574 666 407 666C186 666 23 488 23 277C23 113 129 -3 288 -13L307 -26C431 -111 501 -139 533 -147C559 -154 613 -163 658 -164L666 -141C597 -111 507 -66 430 -11L416 -1C580 42 699 190 699 368ZM601 371C601 227 518 54 381 22L354 40L278 24C175 47 120 145 120 269C120 451 235 631 398 631C540 631 601 521 601 371Z" id="g113-82"></path> </g> <g transform="matrix(.013,0,0,-0.013,96.76,17.979)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,101.27,17.796)"> <use xlink:href="#g113-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,109.76,17.979)"> <use xlink:href="#g113-42"></use> </g> <g transform="matrix(.013,0,0,-0.013,117.67,9.288)"> <use xlink:href="#g113-81"></use> </g> <g transform="matrix(.013,0,0,-0.013,127.88,9.471)"> <use xlink:href="#g113-41"></use> </g> <g 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<svg height="32.9303pt" id="M18-2" version="1.1" viewbox="0 0 130.574 32.9303" width="130.574pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B18-2"> <g transform="matrix(.013,0,0,-0.013,0,17.796)"> <path d="M531 285V335L57 550V494L473 310L57 127V71L531 285ZM531 -40V10H57V-40H531Z" id="g117-94"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.26,17.803)"> <use xlink:href="#g119-73"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,14.82,31.445)"> <use xlink:href="#g50-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,24.43,17.796)"> <use xlink:href="#g113-82"></use> </g> <g transform="matrix(.013,0,0,-0.013,35.99,17.979)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,40.51,17.796)"> <use xlink:href="#g113-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,48.99,17.979)"> <use xlink:href="#g113-42"></use> </g> <g transform="matrix(.013,0,0,-0.013,55.71,17.796)"> <use xlink:href="#g121-106"></use> </g> <g transform="matrix(.013,0,0,-0.013,59,17.796)"> <use xlink:href="#g121-109"></use> </g> <g transform="matrix(.013,0,0,-0.013,65.63,17.796)"> <use xlink:href="#g121-101"></use> </g> <g transform="matrix(.013,0,0,-0.013,75.15,9.288)"> <use xlink:href="#g113-81"></use> </g> <g transform="matrix(.013,0,0,-0.013,85.35,9.471)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,89.87,9.288)"> <use xlink:href="#g113-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,96.78,9.288)"> <use xlink:href="#g113-45"></use> </g> <g transform="matrix(.013,0,0,-0.013,101.93,9.288)"> <use xlink:href="#g113-89"></use> </g> <g transform="matrix(.013,0,0,-0.013,111.87,9.288)"> <use xlink:href="#g113-9"></use> </g> <g transform="matrix(.013,0,0,-0.013,115.29,9.288)"> <use xlink:href="#g113-230"></use> </g> <g transform="matrix(.013,0,0,-0.013,121.76,9.471)"> <use xlink:href="#g113-42"></use> </g> <rect height="0.65243" width="51.2684" x="75.15" y="14.142"></rect> <g transform="matrix(.013,0,0,-0.013,86.21,27.022)"> <use xlink:href="#g113-82"></use> </g> <g transform="matrix(.013,0,0,-0.013,97.78,27.204)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,102.29,27.022)"> <use xlink:href="#g113-90"></use> </g> <g transform="matrix(.013,0,0,-0.013,110.77,27.204)"> <use xlink:href="#g113-42"></use> </g> <g transform="matrix(.013,0,0,-0.013,127.61,17.796)"> <use xlink:href="#g113-47"></use> </g> </g> </svg> <svg class="alt_equation" height="74.7273pt" id="I18" version="1.1" viewbox="0 0 173.19 74.7273" width="173.19pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 0)" xlink:href="#B18-1"></use> <use transform="matrix(1 0 0 1 42.54 41.8)" xlink:href="#B18-2"></use> </svg></span></span><span class="right_break" id="R18"> <svg class="label" height="11.44pt" id="M18-3" version="1.1" viewbox="0 0 15.028 11.44" width="15.028pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B18-3"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <path d="M430 623H48C43 580 39 534 30 478H57C80 544 84 550 152 550H369C286 366 187 178 74 -1L81 -12L156 -2C249 201 343 408 438 611L430 623Z" id="g190-223"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.53,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="74.7273pt" id="IL18" version="1.1" viewbox="0 0 15.09 74.7273" width="15.09pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 31.43)" xlink:href="#B18-3"></use> </svg></span></span></span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">E-Step:计算分布<我>问(Y)</我>=<我>P (Y | X</我>;<我>θ)</我>,这被视为E-step。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">M-Step:优化参数估计的基础上联合概率分布,这被视为M-Step。<span class="equation_break" id="EEq8"><span class="left_break" id="L19"></span><span class="middle_break"><span class="center_break" id="M19"> <svg height="32.9303pt" id="M19-1" version="1.1" viewbox="0 0 9.755 32.9303" width="9.755pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B19-1"> <g transform="matrix(.013,0,0,-0.013,0,17.796)"> <use xlink:href="#g113-230"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,6.47,11.533)"> <use xlink:href="#g50-31"></use> </g> </g> </svg> <svg height="32.9303pt" id="M19-2" version="1.1" viewbox="0 0 171.774 32.9303" width="171.774pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B19-2"> <g transform="matrix(.013,0,0,-0.013,0,17.796)"> <use xlink:href="#g117-34"></use> </g> <g transform="matrix(.013,0,0,-0.013,11.26,17.796)"> <path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 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height="11.44pt" id="M19-3" version="1.1" viewbox="0 0 15.168 11.44" width="15.168pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B19-3"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <path d="M246 635C138 635 67 554 67 471C67 401 116 348 178 315C143 296 103 268 87 252C65 231 42 203 42 157C42 50 128 -12 234 -12C319 -12 432 52 432 169C432 256 370 303 299 344C347 376 373 399 381 408C399 430 408 459 408 487C408 569 341 635 246 635ZM235 603C284 603 334 567 334 483C334 420 306 383 273 358C202 393 142 427 142 503C142 552 173 603 235 603ZM245 21C179 21 122 70 122 163C122 219 155 270 204 300C279 263 352 219 352 144C352 66 308 21 245 21Z" id="g190-152"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.67,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="alt_label" height="57.7196pt" id="IL19" version="1.1" viewbox="0 0 15.23 57.7196" width="15.23pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 22.93)" xlink:href="#B19-3"></use> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content"><i>θ′</我>替换<我>θ</我>。迭代过程将重复进行,直到达到局部最优的估计参数。</span></span></span></p> <h5 id="sec2.7">2.7。多层感知器(MLP)</h5> <p>神经网络,一个有效的函数近似者,常被用来解决在各领域的回归和预测问题。一个通用的多层感知器模型可以由以下3个步骤。<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">初始化网络模型。假设原始函数可以用一组近似的基本功能:<span class="equation_break" id="EEq9"><span class="left_break" id="L20"></span><span class="middle_break"><span class="center_break" id="M20"> <svg height="35.6587pt" id="M20-1" version="1.1" viewbox="0 0 117.034 35.6587" width="117.034pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B20-1"> <g transform="matrix(.013,0,0,-0.013,0,20.415)"> <path d="M584 650H137L131 622C214 614 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<path d="M559 271C559 395 493 448 433 448C370 448 304 394 275 297C259 245 235 121 219 37C157 54 113 110 113 201C113 310 180 386 276 429L265 457C131 410 23 326 23 186C23 83 86 5 211 -10C180 -159 167 -220 158 -252L165 -261C198 -255 236 -246 251 -218L287 -5C421 9 559 119 559 271ZM484 273C484 144 400 40 293 33L352 331C364 389 382 406 405 406C431 406 484 370 484 273Z" id="g113-253"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,73.68,23.547)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.013,0,0,-0.013,78.92,20.598)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,83.43,20.415)"> <use xlink:href="#g113-121"></use> </g> <g transform="matrix(.013,0,0,-0.013,90.7,20.598)"> <use xlink:href="#g113-42"></use> </g> <g transform="matrix(.013,0,0,-0.013,98.15,20.415)"> <path d="M535 230V280H323V490H265V280H52V230H265V-3H323V230H535Z" id="g117-36"></path> </g> <g transform="matrix(.013,0,0,-0.013,108.69,20.415)"> <path d="M391 364C391 409 353 448 295 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602 355 476 355 390C355 371 354 349 351 325C334 310 298 294 261 294C177 294 128 369 128 457C128 516 155 602 231 602Z" id="g190-193"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.84,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">在哪里<我>F</我>是最初的函数,<我>x</我>是输入向量,<我>米</我>是网络突触的数量<svg height="6.1673pt" id="M21" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 9.39034 6.1673" width="9.39034pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-120"></use> </g> </svg>突触的重量,<我>φ</我>分配的基函数在突触,常用的函数和s形曲线(如晒黑<我>h</我>),<我>e</我>是错误的。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">负载样本点对(<我>x</我>,<我>y</我>)和计算预测和真实值之间的误差:<span class="equation_break" id="EEq10"><span class="left_break" id="L22"></span><span class="middle_break"><span class="center_break" id="M22"> <svg height="27.4485pt" id="M22-1" version="1.1" viewbox="0 0 98.164 27.4485" width="98.164pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B22-1"> <g transform="matrix(.013,0,0,-0.013,0,12.205)"> <use xlink:href="#g113-102"></use> </g> <g transform="matrix(.013,0,0,-0.013,9.02,12.205)"> <use xlink:href="#g117-34"></use> </g> <g transform="matrix(.013,0,0,-0.013,20.28,12.211)"> <use xlink:href="#g119-73"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,25.58,25.854)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.013,0,0,-0.013,33.45,12.205)"> <use xlink:href="#g113-120"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,42.38,15.337)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.013,0,0,-0.013,45.43,12.205)"> <use xlink:href="#g113-253"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,52.67,15.337)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.013,0,0,-0.013,57.91,12.388)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,62.42,12.205)"> <use xlink:href="#g113-121"></use> </g> <g transform="matrix(.013,0,0,-0.013,69.69,12.388)"> <use xlink:href="#g113-42"></use> </g> <g transform="matrix(.013,0,0,-0.013,77.14,12.205)"> <use xlink:href="#g117-33"></use> </g> <g transform="matrix(.013,0,0,-0.013,87.67,12.205)"> <use xlink:href="#g113-122"></use> </g> <g transform="matrix(.013,0,0,-0.013,95.2,12.205)"> <use xlink:href="#g113-45"></use> </g> </g> </svg></span></span><span class="right_break" id="R22"> <svg class="label" height="11.44pt" id="M22-2" version="1.1" viewbox="0 0 19.728 11.44" width="19.728pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B22-2"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.49,9.26)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.82,9.26)"> <path d="M248 635C97 635 43 457 43 311C43 153 97 -12 247 -12S450 166 450 312C450 466 397 635 248 635ZM246 603C337 603 361 452 361 312S337 20 248 20S132 172 132 313C132 452 156 603 246 603Z" id="g190-254"></path> </g> <g transform="matrix(.013,0,0,-0.013,15.23,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">在哪里<我>y</我>是样品的真实价值。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">根据误差调整网络突触权重的反馈。调整的通用计算公式<span class="equation_break" id="EEq11"><span class="left_break" id="L23"></span><span class="middle_break"><span class="center_break" id="M23"> <svg height="27.9326pt" id="M23-1" version="1.1" viewbox="0 0 105.194 27.9326" width="105.194pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B23-1"> <g 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transform="matrix(.013,0,0,-0.013,77.69,12.205)"> <use xlink:href="#g113-120"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,86.62,15.336)"> <use xlink:href="#g50-108"></use> </g> <g transform="matrix(.013,0,0,-0.013,91.77,12.205)"> <use xlink:href="#g113-102"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,97.08,15.336)"> <use xlink:href="#g50-108"></use> </g> <g transform="matrix(.013,0,0,-0.013,102.23,12.205)"> <use xlink:href="#g113-45"></use> </g> </g> </svg></span></span><span class="right_break" id="R23"> <svg class="label" height="11.44pt" id="M23-2" version="1.1" viewbox="0 0 17.788 11.44" width="17.788pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B23-2"> <g transform="matrix(.013,0,0,-0.013,0,9.25)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.25)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.9,9.25)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,13.29,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></span></span><span class="list-item"><span class="list-label"></span><span class="list-content">在哪里<我>k</我>是神经元层之后,调整位置。当调整<我>∆w</我>小于预设阈值<我>η</我>这一步将会终止;否则,权重将被更新,这个过程将回到步骤(2)。</span></span></span></p> <h4 id="results-and-discussion">3所示。结果与讨论</h4> <p>的DBN-based RTCPM构造了基于训练数据集(包括264年崩盘数据和noncrash数据)和验证基于验证数据集(包括64年崩盘数据和noncrash数据)。</p> <h5 id="sec3.1">3.1。变量的选择</h5> <p>图<a href="//www.newsama.com/journals/jat/2021/5569143/fig2/" target="_blank">2</a>显示变量重要性排名是由射频方法。很明显,最重要的三个变量是TPI (0.151), V (0.144)、U_V (0.144) (<我>六世</我>> 0.14)。其他变量的相对重要性是小于0.12。因此,TPI、V、U_V被选为影响变量构建DBN模型。令人惊奇的是,能见度和天气发挥有限的作用。这可能是解释事故数据的收集时间(从8月至9月),当有更多的日光和更少的可见性差异(意味着= 21.32公里,SD = 10.90公里)。根据中国的水平能见度等级标准(GB / T 33673 - 2017),当可见性大于或等于10公里,视野被认为是一个很好的水平。因此,总体良好的能见度不崩溃风险在这项研究中有重要贡献。此外,天气分类变量,天气类型(下雨/阳光),作为代理代表天气条件在这项研究中,而不是一个量化的变量,降雨。我们假设之间的关系崩溃风险和降雨可能比天气更明显的类型。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig2"> <div class="floats-partial-content fig-content-fig2"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.002" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.002.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 变量重要性排名由随机森林。</div> </div> </div> <h5 id="sec3.2">3.2。DBN模型建设</h5> <p>DBN模型和不考虑交通流之间的相互依存特征(TPI, V、U_V)都是基于训练数据集的构造。前模型(改进DBN-based RTCPM)是主要目的,而后者之一(原DBN-based RTCPM)是用于比较发达。之前构造的图形结构改进DBN-based RTCPM,三是离散交通流特征根据相应的事故情况下使用ChiMerge算法。每个变量的离散化的状态数仅限于10所以DBN模型可以减少计算复杂度。TPI的离散化范围,V, U_V给出数据<a href="//www.newsama.com/journals/jat/2021/5569143/fig3/" target="_blank">3</a>- - - - - -<a href="//www.newsama.com/journals/jat/2021/5569143/fig5/" target="_blank">5</a>,分别。结果表明,相邻离散化区间在每个变量的特点是可区分的崩溃/ noncrash比率,表明ChiMerge算法进行离散化。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig3"> <div class="floats-partial-content fig-content-fig3"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.003" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.003.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> TPI的离散化范围。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig4"> <div class="floats-partial-content fig-content-fig4"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.004" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.004.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图4</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig4/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 离散化V的范围。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig5"> <div class="floats-partial-content fig-content-fig5"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.005" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.005.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图5</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig5/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> U_V离散化范围。</div> </div> </div> <p>离散化后,电脑算法和专家评估利用调查交通流之间的相互依存特征在一个时间片。交通流特征的动态性是反映通过连接相同的变量时间片2片1。的动态性和相互依赖关系决定了图形结构改进DBN-based RTCPM(图<a href="//www.newsama.com/journals/jat/2021/5569143/fig6/" target="_blank">6</a>)。原DBN-based RTCPM没有考虑交通流特征之间的相关性,及其图形结构直接决定通过连接交通流变量崩溃风险在一个时间片和连接两个时间片(图之间的相同的变量<a href="//www.newsama.com/journals/jat/2021/5569143/fig7/" target="_blank">7</a>)。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig6"> <div class="floats-partial-content fig-content-fig6"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.006" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.006.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图6</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig6/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 图形结构的改进DBN-based RTCPM。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig7"> <div class="floats-partial-content fig-content-fig7"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.007" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.007.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图7</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig7/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 图形结构的原始DBN-based RTCPM。</div> </div> </div> <p>之后的参数学习过程是使用EM算法实现。改进的初始状态DBN-based RTCPM和原始DBN-based RTCPM给出数据<a href="//www.newsama.com/journals/jat/2021/5569143/fig8/" target="_blank">8</a>和<a href="//www.newsama.com/journals/jat/2021/5569143/fig9/" target="_blank">9</a>,分别。观察到,他们的整体交通流状态的概率与崩溃是不同的(分别为36%和42%)没有进入DBN新证据。这种差异表明,比较两种类型的性能的重要性DBN-based RTCPM。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig8"> <div class="floats-partial-content fig-content-fig8"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.008" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.008.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图8</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig8/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 初始状态的改善DBN-based RTCPM。</div> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig9"> <div class="floats-partial-content fig-content-fig9"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.009" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.009.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图9</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig9/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 初始状态的原始DBN-based RTCPM。</div> </div> </div> <h5 id="sec3.3">3.3。模型验证和比较</h5> <p>验证数据集被用来验证DBN模型。当没有新的证据进入DBN,事故风险的边际概率节点DBN模型的初始状态设置为分类阈值来评估模型的性能。然后,每个验证数据集进入单独的模型。事故风险,即。,theposterior probability of crash risk node, relating to the prone traffic condition, were calculated based on the prior probabilities. 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xlink:href="#B24-9"></use> <use transform="matrix(1 0 0 1 0 191.29)" xlink:href="#B24-11"></use> <use transform="matrix(1 0 0 1 0 230.99)" xlink:href="#B24-13"></use> <use transform="matrix(1 0 0 1 0 277.45)" xlink:href="#B24-16"></use> </svg></span></span></p> <div class="floats-partial-section partial-other-wrapper" data-section="tab2"> <div class="floats-partial-content fig-content-tab2"> <table class="table-group" id="tab2"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left"></td> <td class="align_center">预测崩溃</td> <td class="align_center">预测noncrashes</td> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left">实际的崩溃</td> <td class="align_center"><i>T</我><sub>崩溃</sub></td> <td class="align_center"><i>F</我><sub>noncrash</sub></td> </tr> <tr> <td class="align_left">实际noncrashes</td> <td class="align_center"><i>F</我><sub>崩溃</sub></td> <td class="align_center"><i>T</我><sub>noncrash</sub></td> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/tab2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 混淆矩阵。</div> </div> </div> <p>除了总体精度从方程(<a href="#EEq12">12</a>从方程(灵敏度),<a href="#EEq12">13</a>),<我>G</我>——从方程(<a href="#EEq12">17</a>),<我>F</我>测量从方程(<a href="#EEq12">18</a>)被用来比较两种类型的DBNs和MLP的性能。不平衡分类,总体精度指标是不够的由于其无法检查小积极样本;因此,灵敏度被选为辅助指标检查事故分类精度。的<我>F</我>测量精度和召回的调和平均数,是检测碰撞的能力。此外,可以反映在平衡的分类能力<我>G</我>则,敏感性和特异性的几何平均数。并给出了比较结果表<a href="//www.newsama.com/journals/jat/2021/5569143/tab3/" target="_blank">3</a>。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab3"> <div class="floats-partial-content fig-content-tab3"> <table class="table-group" id="tab3"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="5"> <hr></td> </tr> <tr class="thead"> <td class="align_left"></td> <td class="align_center">总体精度</td> <td class="align_center">灵敏度</td> <td class="align_center"><i>F</我>测量</td> <td class="align_center"><i>G</我>则</td> </tr> <tr> <td class="thead-hr" colspan="5"> <hr></td> </tr> <tr> <td class="align_left">原始DBN-based RTCPM</td> <td class="align_center">0.750</td> <td class="align_center">0.313</td> <td class="align_center">0.385</td> <td class="align_center">0.529</td> </tr> <tr> <td class="align_left">改善DBN-based RTCPM</td> <td class="align_center">0.766</td> <td class="align_center">0.688</td> <td class="align_center">0.595</td> <td class="align_center">0.738</td> </tr> <tr> <td class="align_left">MLP-based RTCPM</td> <td class="align_center">0.725</td> <td class="align_center">0.556</td> <td class="align_center">0.476</td> <td class="align_center">0.656</td> </tr> <tr class="table-tr"> <td colspan="5"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/tab3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 两种类型的DBNs和MLP的性能比较。</div> </div> </div> <p>如表所示<a href="//www.newsama.com/journals/jat/2021/5569143/tab3/" target="_blank">3</a>,所有的模型可以达到良好的分类精度。其中,改进DBN-based RTCPM显示最好的总体分类精度,其次是原始DBN-based RTCPM和MLP-based RTCPM。碰撞检测能力的灵敏度指标表明,改进DBN-based RTCPM表现最好的,和相对贫穷的性能被认为在原始DBN-based RTCPM。此外,<我>F</我>测量还建议改进DBN-based RTCPM最好崩溃的预测能力比原来DBN-based RTCPM和MLP-based RTCPM。关于平衡的分类能力,<我>G</我>则透露,改进DBN-based RTCPM比其他模型实现的。所有的比较,结果表明,改进的DBN-based RTCPM可以达到理想的总体预测性能。也证明了这个模型有一个实时有效的监控能力崩溃。与此同时,模型可以保持平衡崩溃并没有崩溃的预测。总之,的预测性能DBN-based RTCPM可以提高占交通流特征的相互依存。</p> <h5 id="sec3.4">3.4。影响分析的灵敏度和强度</h5> <p>调查交通流之间的相互依赖也有助于揭示事故发生的潜在机制,有助于制定实时的风险控制措施。影响分析的灵敏度和强度在专业DBN分析软件,实现精灵,识别最重要的贡献者崩溃风险和最可能的风险传播路径。</p> <h5 id="sec3.5">3.5。敏感性分析</h5> <p>精灵的灵敏度分析可以用来确定哪些节点有更大贡献DBN的目标节点。设置事故风险作为目标节点,进行灵敏度分析,交通流特征的贡献程度崩溃风险呈现在图<a href="//www.newsama.com/journals/jat/2021/5569143/fig10/" target="_blank">10</a>降序排列。结果表明,TPI在时间片2是最敏感的因素,导致崩溃的风险,其次是在时间片2 V、时间片1 TPI等。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig10"> <div class="floats-partial-content fig-content-fig10"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.0010" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.0010.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图10</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig10/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 敏感性分析的结果。</div> </div> </div> <h5 id="sec3.6">3.6。强度的影响分析</h5> <p>影响的强度分析是用来确定最可能的风险传播路径的基础上,相互依存结构有所改善。影响的力量总是从之间的距离计算子节点的条件概率分布在它的父节点的状态。如图<a href="//www.newsama.com/journals/jat/2021/5569143/fig11/" target="_blank">11</a>弧有不同的价值观和厚度,提出影响连接节点之间的力量。最大的累计值表示,最可能的风险传播路径是V(时间片2)⟶(时间片1)⟶U_V(时间片1)⟶TPI(时间片1)当前段⟶崩盘风险。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig11"> <div class="floats-partial-content fig-content-fig11"> <div class="partial-carousel--single-image"> <img alt="" name="5569143.fig.0011" src="https://static-01.hindawi.com/articles/jat/volume-2021/5569143/figures/5569143.fig.0011.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图11</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/jat/2021/5569143/fig11/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 强度的影响分析的结果。</div> </div> </div> <p>合成的灵敏度和强度影响分析的结果可以用来确定最可能的风险传播路径,以及确定传播路径的最敏感的因素。结果表明,序列顺序和重点的实时风险对策应该躺在V(时间片2),TPI(时间片1),V(时间片1)、U_V(时间片1)。</p> <h4 id="conclusions">4所示。结论</h4> <p>本研究旨在构建一个城市高架高速公路利用RTCPM DBN模型来捕获动态性和耦合相互依存在事故发生前交通流特征。模型建立和验证采用交通流数据收集在延安高架高速公路。基于DBN-based RTCPM,影响分析的灵敏度和强度是用来确定最可能的风险传播路径和最敏感的贡献者崩溃的风险。主要结论如下:<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">DBN模型在模型建设过程中,相互依赖是由电脑算法和专家经验,和交通流特征的动态性是表示在时间片采用数据。通过验证,改进DBN-based RTCPM有一个整体的准确性76.6%,68.8%的事故预测精度和崩溃/ noncrash平衡分类精度为73.8%。结果表明,该模型可以实现一个有效的事故预测城市高架高速公路。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">比较原始的DBN-based RTCPM和MLP-based RTCPM建议改进DBN-based RTCPM可以识别事故发生之前交通流特征之间的相关性。比较结果还表明,改进DBN-based RTCPM更适合城市高架高速公路实时崩溃的预测。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">结果显示灵敏度和强度的影响分析,最有可能的风险传播路径是V(时间片2)⟶(时间片1)⟶U_V(时间片1)⟶TPI(时间片1)⟶崩溃风险当前段,事故风险和最敏感的因素在这个路径是V(时间片2),其次是TPI(时间片1),V(时间片1)、U_V(时间片1)。结果表明,连续实时风险对策的制定应该关注这个序列在传播路径中。</span></span></span></p> <p>将来会有两个扩展研究。一方面,模型建立和验证在同一城市高架高速公路;因此,模型的可转让性到另一个城市高架高速公路还没有讨论。另一方面,特定的实时风险对策,如可变限速可以提高调查:崩溃的风险。</p> <h4 id="data-availability">数据可用性</h4> <p>研究数据可用. csv格式的文件。他们可以从相应的作者。</p> <h4 id="conflicts-of-interest">的利益冲突</h4> <p>作者宣称没有利益冲突有关的出版。</p> <h4 id="acknowledgments">确认</h4> <p>这项研究得到了国家自然科学基金(批准号52072071)。</p> </div> <div class="xml-content"> <h4 class="references" id="references">引用</h4> <ol> <li id="B1">c·李,b . 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height="24"><span class="sc-dRFtgE jrsDZP">订单打印副本</span><span class="sc-gkFcWv gWTCXQ">订单</span></span><span class="sc-htoDjs kNoiZu sc-gbOuXE jGPvPL right__icon icon-colored _32KP5uexH-Bgs7Kj8Gk1Tz icon-themeable" title="" font-size="16"></span></a> <div class="sc-hBbWxd kdRRST"> <div class="sc-dyGzUR cHatnS"> <div> 的观点<span>379年</span> </div> <div> 下载<span>299年</span> </div> </div> <div> <div class="sc-drKuOJ kxdGAZ"> <span class="__dimensions_badge_embed__" data-doi="10.1155/2021/5569143" data-style="small_circle"></span>引用</div> </div> </div> <nav class="sc-bRBYWo fXBKFa sc-hUfwpO jHPgUP" id="toolbar__iconNav"> <h3 class="sc-Rmtcm hNPrIr">分享文章</h3> <ul class="sc-hzDkRC bnbfTS"> <li class="iconBorder"><a class="sc-htpNat bUhGXt link" href="https://twitter.com/share?url=https://doi.org/10.1155/2021/5569143&via=Hindawi" aria-label="Sharing on Twitter" rel="noopener noreferrer" target="_blank"><img alt="" class="sc-EHOje jOLhQl socialTwitter" title="" role="presentation" 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