When an urban metro disruption occurs,the urban metro usually operates in a short turning mode and bus bridging is the typical way to relink the disconnected stations.For emergency response models dealing with urban m...When an urban metro disruption occurs,the urban metro usually operates in a short turning mode and bus bridging is the typical way to relink the disconnected stations.For emergency response models dealing with urban metro disruptions,minimizing passenger delay in the bus bridging process and the metro short turning process is usually the optimization objective.In this study,we simultaneously consider the passenger delay in the bus bridging process and the metro short turning process to develop a coordinated emergency response model dealing with urban metro disruptions.The proposed coordinated model is validated in an actual urban metro line using actual passenger boarding demand data.Useful insights in response to urban metro disruptions are obtained.展开更多
Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in...Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.展开更多
为准确把握城市轨道交通客流生成规律,本文从客流成长性视角探究城市轨道交通站点客流与站点周边建成环境之间的交互关系。以上海市城市轨道交通为研究案例,通过人口及就业密度、土地利用、路网密度、出入口数量、介中心性等13个因子刻...为准确把握城市轨道交通客流生成规律,本文从客流成长性视角探究城市轨道交通站点客流与站点周边建成环境之间的交互关系。以上海市城市轨道交通为研究案例,通过人口及就业密度、土地利用、路网密度、出入口数量、介中心性等13个因子刻画建成环境,基于上海市地铁刷卡数据、人口及经济普查、兴趣点(Point of Interest,POI)、道路网络等多源异构数据,分别构建建成环境对轨道交通客流影响的普通最小二乘法(Ordinary Least Square,OLS)模型与极限梯度提升(eXtreme Gradient Boosting,XGBoost)模型进行量化实证分析。结果表明,基于机器学习算法的XGBoost模型比OLS模型具有更好的模型表现。从影响贡献度来看,轨道交通站点建成初期,地铁站点出入口数量(21.9%),常住人口密度(15.9%),路网密度(9.8%)是影响城市轨道交通站点客流的最重要建成环境因素。建成近期,商业设施用地(16.5%)、容积率(11.1%)和就业密度(8.5%)等用地类建成环境变量成为提升城市轨道交通站点客流的关键。建成远期,城市轨道交通站点客流水平取决于出入口数量(18.9%)、商业设施用地开发(16.6%)与换乘线路数量(7.7%)等用地和交通之间的结合水平。研究结果证实了轨道交通客流与站点周边建成环境之间的成长性特征关系及各阶段显著影响客流的建成环境变量,为因时制宜制定城市轨道交通站城一体化开发策略提供了参考。展开更多
基金supported by the National Natural Science Foun-dation of China (Grant No.71871224).
文摘When an urban metro disruption occurs,the urban metro usually operates in a short turning mode and bus bridging is the typical way to relink the disconnected stations.For emergency response models dealing with urban metro disruptions,minimizing passenger delay in the bus bridging process and the metro short turning process is usually the optimization objective.In this study,we simultaneously consider the passenger delay in the bus bridging process and the metro short turning process to develop a coordinated emergency response model dealing with urban metro disruptions.The proposed coordinated model is validated in an actual urban metro line using actual passenger boarding demand data.Useful insights in response to urban metro disruptions are obtained.
基金National Natural Science Foundation of China (Grant No.52178393)the Science and Technology Innovation Team of Shaanxi Innovation Capability Support Plan (Grant No.2020TD005)Science and Technology Innovation Project of China Railway Construction Bridge Engineering Bureau Group Co.,Ltd.(Grant No.DQJ-2020-B07)。
文摘Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.
文摘为准确把握城市轨道交通客流生成规律,本文从客流成长性视角探究城市轨道交通站点客流与站点周边建成环境之间的交互关系。以上海市城市轨道交通为研究案例,通过人口及就业密度、土地利用、路网密度、出入口数量、介中心性等13个因子刻画建成环境,基于上海市地铁刷卡数据、人口及经济普查、兴趣点(Point of Interest,POI)、道路网络等多源异构数据,分别构建建成环境对轨道交通客流影响的普通最小二乘法(Ordinary Least Square,OLS)模型与极限梯度提升(eXtreme Gradient Boosting,XGBoost)模型进行量化实证分析。结果表明,基于机器学习算法的XGBoost模型比OLS模型具有更好的模型表现。从影响贡献度来看,轨道交通站点建成初期,地铁站点出入口数量(21.9%),常住人口密度(15.9%),路网密度(9.8%)是影响城市轨道交通站点客流的最重要建成环境因素。建成近期,商业设施用地(16.5%)、容积率(11.1%)和就业密度(8.5%)等用地类建成环境变量成为提升城市轨道交通站点客流的关键。建成远期,城市轨道交通站点客流水平取决于出入口数量(18.9%)、商业设施用地开发(16.6%)与换乘线路数量(7.7%)等用地和交通之间的结合水平。研究结果证实了轨道交通客流与站点周边建成环境之间的成长性特征关系及各阶段显著影响客流的建成环境变量,为因时制宜制定城市轨道交通站城一体化开发策略提供了参考。
