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701.
Essam Dabbour Marwa Al Awadhi Mina Aljarah Malak Mansoura Murtaza Haider 《国际交通安全学会研究报告》2018,42(4):274-283
With more than 460 roundabouts located in Abu Dhabi, the capital city of the United Arab Emirates, it is imperative to evaluate the safety benefits provided by those roundabouts. In this study, two approaches were used to evaluate those safety benefits. The first approach is by measuring the 85th percentile operating speeds at a sample of 18 roundabouts in Abu Dhabi to determine whether the measured operating speeds conform to what is recommended by design guides. The second approach is by using a questionnaire to measure how drivers in Abu Dhabi perceive safety when driving at roundabouts and to measure their level of knowledge regarding the rules pertaining to driving at roundabouts. The study found that operating speeds at Abu Dhabi roundabouts typically exceed those recommended by design guides. The study also found that only 4.1% of the drivers interviewed demonstrated a comprehensive understanding of the rules pertaining to driving at roundabouts. Ordinal regression modeling was used to identify driver groups in need for more awareness of the rules to negotiate roundabouts in Abu Dhabi. The study found that the driver group in most need for more awareness is typically young and middle-age male drivers (below the age of 46?years) with less driving experience in countries where roundabouts are common. The questionnaire also revealed that despite the operational benefits provided by roundabouts (in terms of reduced delay), drivers do not prefer to drive at Abu Dhabi roundabouts, which might be explained by drivers' perception of Abu Dhabi roundabouts being not safe. 相似文献
702.
In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms
(errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential
temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR
model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable
to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates
in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the
model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing
severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated
with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much
lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations
in the presence of temporal and spatial effects.
Ms. Xiaokun Wang is a doctoral student in the Department of Civil, Architectural and Environmental Engineering at the University of Texas at Austin. She received her B.S. and M.S. degrees at Tsinghua University, China. Her research topics range from travel demand modeling and integrated land use-transportation planning, to spatial econometrics, network analysis, and traffic safety analysis. She is a fellow of the International Road Federation. Dr. Kara Kockelman is a Associate Professor of Civil, Architectural & Environmental Engineering and the William J. Murray Jr. Fellow at the University of Texas, Austin. She holds a PhD, MS, and BS in Civil Engineering, a Masters of City Planning, and a minor in Economics from the University of California at Berkeley. She is Chair of the Transportation Research Board’s Committee on Travel Survey Methods. Her primary research interests include the statistical modeling of urban systems (including models of travel behavior, trade, and location choice), economic impacts of transport policy, crash occurrence and consequences, and transport policy-making. 相似文献
Kara M. Kockelman (Corresponding author)Email: |
Ms. Xiaokun Wang is a doctoral student in the Department of Civil, Architectural and Environmental Engineering at the University of Texas at Austin. She received her B.S. and M.S. degrees at Tsinghua University, China. Her research topics range from travel demand modeling and integrated land use-transportation planning, to spatial econometrics, network analysis, and traffic safety analysis. She is a fellow of the International Road Federation. Dr. Kara Kockelman is a Associate Professor of Civil, Architectural & Environmental Engineering and the William J. Murray Jr. Fellow at the University of Texas, Austin. She holds a PhD, MS, and BS in Civil Engineering, a Masters of City Planning, and a minor in Economics from the University of California at Berkeley. She is Chair of the Transportation Research Board’s Committee on Travel Survey Methods. Her primary research interests include the statistical modeling of urban systems (including models of travel behavior, trade, and location choice), economic impacts of transport policy, crash occurrence and consequences, and transport policy-making. 相似文献
703.
为探究道路交通条件对路段人行横道机动车让行的影响,通过无人机视频调查,采集了20处路段人行横道机动车通行过程数据,在对logistic模型应用条件进行检验的基础上,建立了路段人行横道机动车让行概率模型,并检验了模型的准确性。模型分析结果表明,安全间距、车速、监控、车道数、行人数量和行人位置对让行率具有显著影响;通过增设监控和行人中央驻足区可分别提高让行率为原来的3.700倍和4.339倍,当车道数大于4时,即使采取监控措施让行率仍将小于30%,应采用信号控制。 相似文献
704.
以京港澳高速公路(G4)粤境北段3年发生的1 354起交通事故为研究对象,将基础数据根据路段长度一致、曲线半径一致和坡度一致划分路段单元对基础数据进行处理,从道路线形和环境条件2个方面选取13个自变量,分别采用负二项(Negative Binomial,NB)回归模型和非线性负二项(Nonlinear Negative Binomial,NNB)回归模型建立交通事故起数预测模型,根据模型的拟合优度和预测准确性对比分析负二项回归和非线性负二项回归模型的优劣,并找出影响交通事故起数的显著自变量,分析显著自变量对交通事故起数的影响程度。研究结果表明:无论采用上述何种路段划分方法,非线性负二项回归模型构建的交通事故起数预测模型均优于负二项回归模型;采用坡度一致划分方法明显优于路段长度一致和曲线半径一致划分方法,更适合应用于山区高速公路交通事故数预测研究;从显著变量相关性来看,路段长度、相邻路段坡度变化值、弯坡组合、曲率、是否存在隧道路段以及是否为易结冰和起雾路段均是非线性模型的显著影响因素。 相似文献
705.
为了提高由盾构施工引起的软硬不均地层地表沉降预测的准确性,建立基于粗糙集-支持向量回归(RS-SVR)的地表沉降预测模型,并将该模型应用于实际地铁隧道工程的地表沉降预测中。首先,根据特定地质条件,从几何因素、地层因素和盾构施工因素选取影响地表沉降的条件属性,采用粗糙集理论的Pawlak属性重要度方法删除冗余数据,获取影响地表沉降的最优条件属性集。在此基础上,基于支持向量回归(SVR)建立RS-SVR地表沉降预测模型,并与没有经过属性约简的SVR模型进行对比分析。为了比较不同核函数对SVR模型的影响,RS-SVR和SVR模型分别选取径向基函数(RBF)、Sigmoid函数、Polynomial函数作为核函数对训练样本及测试样本进行回归预测。最后,利用佛山地铁2号线南湖区间上软下硬地层的20组地表沉降监测数据,对该模型予以试算。研究结果表明:将选取的影响地表沉降的12项条件属性约简为包含7项的最优条件属性集,分别为硬层比、黏聚力、内摩擦角、土仓压力、总推力、刀盘扭矩以及掘进时间,地表沉降分类结果与约简前保持一致;同类模型进行横向对比时,RBF作为核函数的RS-SVR模型和SVR模型预测误差分别为5.54%、13.10%,均低于以Sigmoid函数和Polynomial函数作为核函数时的预测误差;以同种核函数进行纵向对比时,RS-SVR模型预测误差分别为5.54%、11.48%、13.26%,均低于SVR模型预测误差的13.10%、15.71%、19.68%。 相似文献
706.
针对集装箱、散货、油及杂货4类船舶搜集了一定数量的统计样本,通过数理统计方法对这些样本进行了船舶排水量与载重吨指标之间的相关性分析,并建立了回归方程。 相似文献
707.
Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data streams.This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is detected.The intensive experimental results demonstrate the effectiveness of this algorithm. 相似文献
708.
709.
为揭示新冠疫情背景下公交客流量变化的空间影响因素,以疫情前后公交站点层面客流变化量为因变量,以建成环境、病毒感染情况及病毒传播途径等指标为自变量,构建新冠疫情与建成环境对公交客流量共同影响的线性回归(Ordinary Least Squares, OLS)模型与梯度提升回归树(Gradient Boosting Regression Trees, GBRT)模型。以广州市为实证对象,基于公交IC卡数据、兴趣点数据(Point of Interest, POI)及道路网络数据等多源异构数据进行模型实证分析。结果表明:考虑非线性效应的GBRT模型比OLS模型具有更好的拟合度;同时,常规公交站点的公交线路数量(22.02%)和到市中心距离(13.56%)是影响疫情背景下公交客流量变化的最重要因素,片区病毒感染与传播情况对疫情防控常态化时期的公交客流量作用有限,居民日常公交出行已经从疫情的影响下逐渐恢复。 相似文献