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Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage. 相似文献
33.
There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks. 相似文献
34.
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
35.
This paper presents a multiple discrete-continuous econometric structure to model the daily time-investment decisions of couples
in solo- and joint-discretionary activities incorporating intra-personal and inter-personal inter-dependencies. The empirical
model was estimated using data from the 2000 Bay Area Travel Survey. The results indicate evidence of the positive impact
of vehicle availability on independent activity participation and the negative impacts of the presence of children and mandatory
time investments on the joint discretionary-activity engagement of the spouses. In addition, we also find the mandatory- and
maintenance-activity-participation characteristics of the spouse to influence the discretionary activity choices of individuals.
Finally, the analysis also indicates a strong impact of common unobserved factors on the decisions of couples. From a policy
analysis perspective, these results imply that demand-management actions directly impacting one adult could also result in
changes to the activity patterns of his/her spouse and to changes in joint activity participation characteristics.
Dr. Sivaramakrishnan Srinivasan is an Assistant Professor in the Department of Civil and Coastal Engineering at the University of Florida. His research interests
include travel-behavior analysis, activity-based travel-demand modeling, and the application of advanced econometric methods
for transportation problems.
Dr. Chandra R. Bhat has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of
which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers
(ASCE). 相似文献
36.
Sales tax measures passed at the local level and dedicated to transportation projects have become increasingly popular in
the United States. While revenues from fuel taxes stagnate, growth of local transportation sales taxes (LTSTs), most approved
in local elections, has led to a gradual shift of the financial base for transportation projects away from user fees and toward
broader-based taxes. In this study, the relationship between voter support and the social, political, and geographic characteristics
of the voters is explored. Using precinct-level voting data and census demographic data for three local transportation sales
tax elections in Sonoma County, in the San Francisco Metropolitan area of California, regression models were constructed to
analyze this relationship. In addition, the relationship between the outcomes of the three measures was explored to better
understand which transportation projects might have garnered more support for the successful measure. It was found that the
closer voters lived to the transportation projects to be funded, the greater their support. Higher incomes were also positively
related to support, controlling for other variables. Political leanings were found to affect support, with the direction of
the effect dependent upon the project list in each measure’s expenditure plan. Finally, it appears that the latest measure,
which passed successfully, benefited greatly from its multi-modal expenditure plan. 相似文献
37.
Abstract The number of bus‐based Park and Ride (P&R) schemes in the UK has grown substantially over the past 40 years as a result of its encouragement by the Government as a tool to deal with increasing traffic congestion and traffic‐related pollution. The aim of this article is to analyse the degree to which P&R is effective in the contemporary policy context. The authors identify phases of development of P&R since its emergence as a local solution to transport capacity constraints in historic towns. Policy goals are identified against which a review of literature is used to highlight its effectiveness. It is concluded that P&R may increase the distance travelled by its users due to low load factors on dedicated buses, public transport abstraction and trip generation, although it is highlighted that there are areas in which further research is required to clarify its impacts. 相似文献
38.
针对传统控制方式难以准确完成AGV轨迹跟踪任务这一问题,提出一种基于自适应模糊控制的轨迹跟踪方法.首先建立AGV的运动学模型,并基于李雅普诺夫第二法设计控制律.其次,以期望轨迹与实际轨迹的位姿偏差作为输入,以控制律中的比例因子作为输出,设计自适应模糊控制器.最后,使用Matlab/Simulink对设计的控制系统进行仿... 相似文献
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