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311.
数值仿真与模型风洞试验相结合研究了典型工况下两车队列中尾车发动机舱盖气动特性和两车间隔区域的流场,对比了缩比模型和实车模型对应雷诺数下车辆队列的流动形态。缩比模型仿真结果与风洞试验结果一致表明采用数值方法的可行。对比不同雷诺数下车辆队列气动特性发现,缩比模型与实车模型发动机舱盖表面平均静压分布基本相同,但在纵向对称面上,实车模型的前车尾迹比缩比模型更加上扬,底部区域气流速度更高。非定常条件下,实车模型前车尾涡相对尺度明显小于缩比模型,且扩散得更充分,尾迹区涡的分布状态更加混沌,发动机舱盖表面脉动能量的分布更加混乱。涡在两车间隔区域的运动并非简单的移动,而是一个由涡破裂、涡配对和涡融合构成的复杂过程。 相似文献
312.
在全国高铁网络逐步构成的背景下,旅游城市高铁站站前广场在高铁运营后逐渐显现出了设施配套不足、交通组织混乱等问题.以黄山北站为例,通过分析黄山北站客流特征、站前广场交通设施现状、交通组织及运行,对黄山北站客流进行了预测,对交通设施规模进行了测算.根据预测结果,提出了交通改善策略,并从设施布局、交通组织等方面提出了近期和远期的具体交通改善方案. 相似文献
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《Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility》2012,50(5):637-652
Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints. 相似文献
315.
A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories
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This paper shows the relationship between flow, generalized origin–destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra‐like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
316.
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
317.
Walking has been highlighted as an independent transportation mode as well as an access/egress mode to/from public transit to encourage the use of more sustainable transport systems. However, walking does not seem to have priority over other transportation modes, especially in areas where various modes of movement are in conflict. The pedestrian push-button system seems to be a solution to distribute the right of way. The focus of this study is on the performance issue of the pedestrian push-button. Specifically, this study deals with issues related to mid-block crossings and attempts to answer two questions: whose waiting time is longer at pre-timed and push-button crossings, pedestrians, or vehicles? and which system – pre-timed or push-button – is better in terms of total waiting time? According to our simulation analyses, if the pedestrian flow rate is less than 120, 85, and 70 ped/h for two-, three-, and four-lane roads, respectively, the push-button system is recommended. 相似文献
318.
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
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ABSTRACTIn this paper, we analyze the travel patterns of Iranian women, where typical patriarchal views and specific social and cultural norms may differ from the patterns of those in western societies. In addition to inherent psycho-physical gender differences, women in Iran can face special constraints forcing them not to be involved in all activity-travel patterns that people in developed countries usually undertake. We pay special attention to the role of marital and employment status on women’s activity-travel patterns. To this end, we develop a joint mode and daily activity pattern (DAP) discrete choice model, which is a two-level mixed nested Logit. The upper nest of the proposed model embodies women’s DAP choices, and the lower nest belongs to the mode choices. In this paper, we try to show how different factors in a patriarchal Muslim society like Iran affect or restrict women’s type and structure of activity-travel patterns. 相似文献