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91.
在进行地铁隧道施工对邻近建筑物影响后安全风险分析时,因统计数据缺乏或不可得等原因造成实际基本概率难以用确定值表示,存在一定的模糊性。综合模糊集理论适用于处理模糊信息,以及贝叶斯网络适用于不确定知识推理等方面的优势,提出了基于模糊贝叶斯网络的定量安全风险分析方法,建立了地铁盾构隧道施工环境下邻近建筑物安全风险决策模型,预测地铁盾构隧道施工诱发邻近建筑物破坏的可能性;根据模糊重要度计算结果进行安全致险因素敏感性分析;以辨识关键致险因素;并结合工程实例验证该方法的适用性。 相似文献
92.
结合列车全自动运行系统的弹性评估,提供一种基于贝叶斯网络模型的评估方法。根据列车故障前后的
性能变化曲线,总结系统弹性的 4 种能力,探究影响这些能力的相关因素,为影响因素的定量选取合适的评价指
标,作为贝叶斯网络的最底层节点。对指标制定打分表,获取专家的打分结果作为指标层的先验概率输入,同时
选择恰当的隶属度公式作为条件概率的计算方法,获得贝叶斯网络的条件概率表,建立列车全自动运行系统的贝
叶斯网络模型,定量评估系统弹性,同时对目标节点进行敏感度分析,提出提升弹性的改进建议。 相似文献
93.
铁路系统转辙机维修方式仍沿用故障修模式,无法提高故障排除速度和准确性,提出利用改进遗传算法优化贝叶斯网络的方法建立故障诊断模型。利用遗传算法搜索能力强,不易陷入局部最优的特点,采用连接矩阵代替网络结构的编码方式,通过修改适应度函数、更新遗传操作方式、修正非法图等方法改进遗传算法,最终解决贝叶斯网络结构学习算法容易缩小搜索空间及易陷入局部最优的缺点。最后利用标准Asia网络验证本文算法比K2和GA算法有更好的搜索结果和更快的收敛速度,以道岔失去表示故障为例验证改进算法对转辙机故障诊断的优越性。 相似文献
94.
给出了PLC控制系统模拟客车检测列车供电柜电源和集控器的方法,在此基础上介绍了一种S7-1200 PLC与SIMATIC net OPC实时通信的软、硬件设计方案.实际案例应用结果表明,该方法简单可靠,能够满足列车供电柜的性能检测要求. 相似文献
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96.
Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation. 相似文献
97.
Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model
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The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
98.
Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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