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711.
连续刚构桥施工阶段地震响应分析研究 总被引:1,自引:0,他引:1
为研究不同地震作用对大跨连续刚构桥建造过程中不同施工阶段的响应,结合新颁布的公路桥梁抗震设计细则,以南宁仙葫大桥为例建有限元模型,采用反应谱法及动态时程分析方法,输入不同烈度的地震作用,对采用悬臂法施工的不同施工阶段的地震响应进行分析对比,结果表明连续刚构桥建造过程中悬臂端位移、主梁根部弯矩及桥墩底部弯矩变化较大,研究结果可以对大跨度连续刚构桥的动力特性以及地震反应特性有更深入的了解。 相似文献
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713.
David A. Hensher William H. Greene 《Transportation Research Part B: Methodological》2011,45(7):954-972
In recent years we have seen important extensions of logit models in behavioural research such as incorporation of preference and scale heterogeneity, attribute processing heuristics, and estimation of willingness to pay (WTP) in WTP space. With rare exception, however, a non-linear treatment of the parameter set to allow for behavioural reality, such as embedded risk attitude and perceptual conditioning of occurrence probabilities attached to specific attributes, is absent. This is especially relevant to the recent focus in travel behaviour research on identifying the willingness to pay for reduced travel time variability, which is the source of estimates of the value of trip reliability that has been shown to take on an increasingly important role in project appraisal. This paper incorporates, in a generalised non-linear (in parameters) logit model, alternative functional forms for perceptual conditioning (known as probability weighting) and risk attitude in the utility function to account for travel time variability, and then derives an empirical estimate of the willingness to pay for trip time variability-embedded travel time savings as an alternative to separate estimates of time savings and trip time reliability. We illustrate the richness of the approach using a stated choice data set for commuter choice between unlabelled attribute packages. Statistically significant risk attitude parameters and parameters underlying decision weights are estimated for multinomial logit and mixed multinomial logit models, along with values of expected travel time savings. 相似文献
714.
A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction 总被引:1,自引:0,他引:1
Xiang Fei Chung-Cheng Lu Ke Liu 《Transportation Research Part C: Emerging Technologies》2011,19(6):1306-1318
This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions. 相似文献
715.
Bin Yu William H.K. Lam Mei Lam Tam 《Transportation Research Part C: Emerging Technologies》2011,19(6):1157-1170
Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes. 相似文献
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为研究桥梁在移动车辆荷载作用下的动力特性和承载能力,以某连续梁桥为计算实例,采用大型通用有限元软件ANSYS建立了质量-弹簧的车辆模型和桥梁结构的有限元模型,用时程分析法分析了车辆荷载作用下连续梁桥的动力响应特征,着重探讨了车辆刚度、车辆质量、行车速度等车辆荷载因素对连续梁桥动力响应的影响规律,并提出相应结论。 相似文献
718.
分析了潜艇打击远程目标过程各阶段的时间延迟,采用误差合成的方法建立了反舰导弹捕捉概率简化模型,并据以进行了量化分析,提出了相应建议。 相似文献
719.
通过使用空空导弹雷达导引头箔条干扰仿真软件,采用目标飞机运动模型、箔条干扰特性、导弹运动模型,对箔条诱骗空空导弹雷达导引头的全过程进行作战仿真,分析出箔条干扰弹投放最佳时机。 相似文献
720.
分析了公交站点间车辆运行过程,将行程预测时间划分为交叉口排队等待时间、路段行驶时间和停站时间3个部分,利用交通波理论和延误三角形,分别建立了无公交专用车道和有公交专用车道2种情况下排队等待时间的动态预测模型;根据乘客到站规律和上下车规律,提出了公交车进站停靠时间模型;针对无公交专用车道条件下的时间预测方法进行了实例演算.实验数据表明,基于交通波行程时间预测方法具有较高的精度,可以满足站点间行程时间预报要求. 相似文献