text: (window["webpackJsonp"]=window["webpackJsonp"]||[]).push([["chunk-adc650f8"],{"1de3":function(e,r,t){"use strict";t("95b8")},"7d8c":function(e,r,t){},"95b8":function(e,r,t){},a5c5:function(e,r,t){"use strict";t.r(r);var a=function(){var e=this,r=e.$createElement,t=e._self._c||r;return t("div",{staticClass:"container"},[e._m(0),t("div",{staticClass:"content"},[t("div",[t("p",{staticClass:"title"},[e._v("模型列表")]),t("a-divider",{staticStyle:{margin:"10px 0","background-image":"linear-gradient(to right,  rgb(103, 179, 241),  rgb(103, 179, 241), #f6f6f6, #f6f6f6)"}}),t("p",{staticStyle:{"text-indent":"2em"}},[e._v("截至目前，LibCity支持4类主流的时空数据挖掘任务：交通流量预测、交通速度预测、交通需求量预测和轨迹下一条预测。LibCity已经实现了18个常用的模型。我们在下表中总结了这些模型的信息。")]),t("a-divider",{staticStyle:{"font-size":"24px"}},[e._v("交通状态预测")]),t("model-table-cn",{attrs:{content:e.state}}),t("br"),t("a-divider",{staticStyle:{"font-size":"24px"}},[e._v("轨迹位置预测")]),t("model-table-cn",{attrs:{content:e.location}}),t("br")],1)])])},i=[function(){var e=this,r=e.$createElement,t=e._self._c||r;return t("div",{staticClass:"header"},[t("div",{staticStyle:{"padding-top":"20px",color:"white"}},[t("p",{staticStyle:{margin:"20px 0 30px 130px","font-size":"60px"}},[e._v("模型")]),t("p",{staticStyle:{margin:"0px 0 20px 130px","font-size":"30px"}},[e._v(" LibCity支持4类主流的时空数据挖掘任务，并实现了18个常用的模型。 ")])])])}],o=function(){var e=this,r=e.$createElement,t=e._self._c||r;return t("table",[e._m(0),t("tbody",e._l(e.content,(function(r){return t("tr",{key:r.model},[t("td",{attrs:{width:"20%"}},[t("a",{attrs:{href:r.mlink,target:"_blank"}},[e._v(e._s(r.model))])]),t("td",{attrs:{width:"20%"}},[e._v(e._s(r.from))]),t("td",{attrs:{width:"10%"}},[e._v(e._s(r.year))]),t("td",{attrs:{width:"50%"}},[t("a",{attrs:{href:r.plink,target:"_blank"}},[e._v(e._s(r.paper))])])])})),0)])},n=[function(){var e=this,r=e.$createElement,t=e._self._c||r;return t("thead",{staticStyle:{"font-size":"16px"}},[t("tr",[t("th",[e._v("模型")]),t("th",[e._v("会议/期刊")]),t("th",[e._v("年份")]),t("th",[e._v("论文")])])])}],l={name:"table",props:{content:{type:Array,required:!0}},data:function(){return{}}},p=l,s=(t("e74e"),t("2877")),c=Object(s["a"])(p,o,n,!1,null,null,null),m=c.exports,d=[{model:"LSTPM",mlink:"#",from:"AAAI",year:"2020",paper:"Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation",plink:"https://ojs.aaai.org//index.php/AAAI/article/view/5353"},{model:"DeepMove",mlink:"#",from:"WWW",year:"2018",paper:"DeepMove: Predicting Human Mobility with Attentional Recurrent Networks",plink:"https://dl.acm.org/doi/10.1145/3178876.3186058"},{model:"Serm",mlink:"#",from:"CIKM",year:"2017",paper:"SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories",plink:"https://dl.acm.org/doi/10.1145/3132847.3133056"},{model:"ST-RNN",mlink:"#",from:"AAAI",year:"2016",paper:"Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts",plink:"https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/11900/11583"},{model:"FPMC",mlink:"#",from:"WWW",year:"2010",paper:"Factorizing Personalized Markov Chains for Next-Basket Recommendation",plink:"https://dl.acm.org/doi/10.1145/1772690.1772773"}],f=[{model:"MTGNN",mlink:"#",from:"SIGKDD",year:"2020",paper:"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks",plink:"https://arxiv.org/abs/2005.11650"},{model:"TGC-LSTM",mlink:"#",from:"TITS",year:"2020",paper:"Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting",plink:"https://ieeexplore.ieee.org/abstract/document/8917706"},{model:"T-GCN",mlink:"#",from:"TITS",year:"2020",paper:"T-gcn: A temporal graph convolutional network for traffic prediction",plink:"https://ieeexplore.ieee.org/abstract/document/8809901/"},{model:"GWNET",mlink:"#",from:"IJCAI",year:"2019",paper:"Graph Wavenet for Deep Spatial-Temporal Graph Modeling",plink:"https://arxiv.org/abs/1906.00121"},{model:"STGCN",mlink:"#",from:"IJCAI",year:"2018",paper:"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting",plink:"https://www.ijcai.org/Proceedings/2018/0505"},{model:"DCRNN",mlink:"#",from:"ICLR",year:"2018",paper:"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting",plink:"https://arxiv.org/abs/1707.01926"},{model:"RNN",mlink:"#",from:"-",year:"-",paper:"-",plink:"-"}],g=[{model:"AGCRN",mlink:"#",from:"NIPS",year:"2020",paper:"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting",plink:"https://arxiv.org/abs/2007.02842"},{model:"Multi-STGCnet",mlink:"#",from:"IJCNN",year:"2020",paper:"Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting",plink:"https://ieeexplore.ieee.org/abstract/document/9207049"},{model:"ASTGCN",mlink:"#",from:"AAAI",year:"2019",paper:"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting",plink:"https://www.aaai.org/ojs/index.php/AAAI/article/view/3881"}],k=[{model:"CCRNN",mlink:"#",from:"AAAI",year:"2021",paper:"Coupled Layer-wise Graph Convolution for Transportation Demand Prediction",plink:"https://arxiv.org/abs/2012.08080"},{model:"ST-ED-RMGC",mlink:"#",from:"Emerging Technologies",year:"2021",paper:"Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network",plink:"https://www.sciencedirect.com/science/article/pii/S0968090X20307580"},{model:"STG2Seq",mlink:"#",from:"IJCAI",year:"2019",paper:"Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting",plink:"https://arxiv.org/abs/1905.10069"},{model:"DMVST-Net",mlink:"#",from:"AAAI",year:"2018",paper:"Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction",plink:"https://arxiv.org/abs/1802.08714"},{model:"GraphLSTM",mlink:"#",from:"TITS",year:"2018",paper:"Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts",plink:"https://ieeexplore.ieee.org/abstract/document/9099450/"},{model:"DPFE",mlink:"#",from:"Emerging Technologies",year:"2018",paper:"Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data",plink:"https://www.sciencedirect.com/science/article/pii/S0968090X18302948"}],h=[{model:"AGCRN",mlink:"#",from:"NIPS",year:"2020",paper:"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting",plink:"https://arxiv.org/abs/2007.02842"},{model:"MSTGCN",mlink:"#",from:"AAAI",year:"2019",paper:"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting",plink:"https://www.aaai.org/ojs/index.php/AAAI/article/view/3881"},{model:"ASTGCN",mlink:"#",from:"AAAI",year:"2019",paper:"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting",plink:"https://www.aaai.org/ojs/index.php/AAAI/article/view/3881"},{model:"MTGNN",mlink:"#",from:"SIGKDD",year:"2020",paper:"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks",plink:"https://arxiv.org/abs/2005.11650"},{model:"TGC-LSTM",mlink:"#",from:"TITS",year:"2020",paper:"Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting",plink:"https://ieeexplore.ieee.org/abstract/document/8917706"},{model:"T-GCN",mlink:"#",from:"TITS",year:"2020",paper:"T-gcn: A temporal graph convolutional network for traffic prediction",plink:"https://ieeexplore.ieee.org/abstract/document/8809901/"},{model:"GWNET",mlink:"#",from:"IJCAI",year:"2019",paper:"Graph Wavenet for Deep Spatial-Temporal Graph Modeling",plink:"https://arxiv.org/abs/1906.00121"},{model:"STGCN",mlink:"#",from:"IJCAI",year:"2018",paper:"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting",plink:"https://www.ijcai.org/Proceedings/2018/0505"},{model:"DCRNN",mlink:"#",from:"ICLR",year:"2018",paper:"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting",plink:"https://arxiv.org/abs/1707.01926"},{model:"ACFM",mlink:"#",from:"ACM MM",year:"2018",paper:"Attentive Crowd Flow Machines",plink:"https://dl.acm.org/doi/10.1145/3240508.3240681"},{model:"ST-ResNet",mlink:"#",from:"AAAI",year:"2017",paper:"Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction",plink:"https://arxiv.org/abs/1610.00081"},{model:"RNN",mlink:"#",from:"YAC",year:"2016",paper:"Using LSTM and GRU neural network methods for traffic flow prediction",plink:"https://ieeexplore.ieee.org/abstract/document/7804912"}],u={data:function(){return{location:d,speed:f,flow:g,demand:k,state:h}},components:{modelTableCn:m}},w=u,v=(t("1de3"),Object(s["a"])(w,a,i,!1,null,"2a30b536",null));r["default"]=v.exports},e74e:function(e,r,t){"use strict";t("7d8c")}}]);
  //# sourceMappingURL=chunk-adc650f8.62b0ece5.js.map