首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   601篇
  免费   16篇
公路运输   65篇
综合类   267篇
水路运输   127篇
铁路运输   44篇
综合运输   114篇
  2024年   1篇
  2023年   3篇
  2022年   8篇
  2021年   9篇
  2020年   8篇
  2019年   6篇
  2018年   15篇
  2017年   11篇
  2016年   23篇
  2015年   29篇
  2014年   34篇
  2013年   37篇
  2012年   35篇
  2011年   36篇
  2010年   46篇
  2009年   37篇
  2008年   29篇
  2007年   47篇
  2006年   40篇
  2005年   40篇
  2004年   21篇
  2003年   13篇
  2002年   19篇
  2001年   13篇
  2000年   12篇
  1999年   12篇
  1998年   14篇
  1997年   2篇
  1996年   1篇
  1995年   5篇
  1994年   6篇
  1993年   1篇
  1991年   2篇
  1988年   2篇
排序方式: 共有617条查询结果,搜索用时 31 毫秒
51.
铁路路网运输能力可靠性是指在规定的条件(路网结构、固定设备、移动设备及一定人力与运输组织水平)下和规定的时间内,考虑日常随机因素以及不可抗力因素等的影响,铁路路网所能提供的各种服务能满足OD需求波动的能力.本文在分析了国内外路网能力可靠性研究和铁路运输的特点上,提出了铁路路网能力可靠性的概念,并构建了相关路网能力的计算...  相似文献   
52.
文章以复杂网络知识为基础,通过对无标度网络及度分布平均场的介绍,利用网络中度与集群系数的联系,研究因特网信息包传播过程中的一般拥塞模型,并解析无标度网络中的拥塞量与时间的关系。这样就能够预测拥塞量,以便及时有效执行相关措施  相似文献   
53.
Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.  相似文献   
54.
The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.  相似文献   
55.
Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.  相似文献   
56.
针对船用锅炉人因安全性分析中存在的知识不确定性,采用D-S证据理论对多专家信息进行融合并建立考虑人因的贝叶斯网络,得到节点条件概率的区间表示形式.经加权平均后代入贝叶斯网络计算,与面向对象贝叶斯网络和FTA等方法的对比显示,该方法能够更加有效地融合不同专家信息,也更为符合工程实际.  相似文献   
57.
Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.  相似文献   
58.
Elman递归神经网络在结构分析中的应用   总被引:1,自引:0,他引:1  
给出了Elman动态递归神经网络的网络结构和基本原理。基于Elman递归神经网络能够逼近任意非线性函数的特点,提出了一种基于Elman递归神经网络建立结构分析模型的方法。利用Elman递归神经网络对桁架进行建模,真实地反映了桁架结构的动态特性。  相似文献   
59.
Most routing protocols for sensor networks try to extend network lifetime by minimizing the energy consumption, but have not taken the network reliability into account. An energy-aware, load-balancing and fault-tolerant routing scheme, termed as ELFR was propsed to adapt to the harsh environment. First a network robustness model was presented. Based on this model, the route discovery phase was designed to make the sensors to construct into a hop-leveled network which is mesh structure. A cross-layer design was adopted to measure the transmission delay so as to detect the failed nodes. The routing scheme works with acknowledge (ACK) feedback mechanism to transfer control messages to avoid producing extra control overhead messages. When nodes fail, the new healthy paths will be selected locally without rerouting. Simulation results show that our scheme is much robust, and it achieves better energy efficiency, load balancing and maintains good end-to-end delay.  相似文献   
60.
鉴于模糊神经网络具有良好的非线性特性、学习能力、自适应能力和抗干扰能力,本文将模糊神经网络技术引入到高速公路入口匝道控制中。提出一种基于GA和BP算法的模糊神经网络控制器,并对控制器进行了详细设计。设计过程主要分为三部分:输入输出参数的选择、模糊神经网络的结构设计以及基于GA-BP的学习算法设计。最后,使用MATLAB软件对其进行了仿真。仿真结果表明,本文提出的方法是有效的,较之基于BP的模糊神经网络控制和ALINEA控制,能更好地稳定主线交通流密度。  相似文献   
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号