基于径向基函数神经网络的城市道路路段行程时间实时预测模型

提出利用径向基函数（RBF）神经网络方法对城市道路路段行程时间进行建模NN,并结合线圈和视频实测数据进行仿真分析,以实际行程时间和模型输出的行程时间预测值比较验证了模型的合理性。并将RBF神经网络方法与BP神经网络方法进行比较,结果表明RBF神经网络相对于BP神经网络训练时间短,且预测精度更高。     

连续刚构桥施工阶段地震响应分析研究

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。     

海上舰艇编队高精度时间同步系统设计

针对舰艇编队遂行协同打击任务的高精度时统需求,提出一种超宽带CDMA体制的时统系统设计方案。该系统应用TOA算法,基于超宽带高时间分辨率波形设计,采用可靠同步、干扰抑制和FFT校频等关键算法,实现复杂电磁环境下作战节点快速移动中可靠高精度时间统一,时间同步精度达10 ns。关键算法仿真和试验样机测试证明该系统设计和关键算法合理有效。     

Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings

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.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

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.     

Bus arrival time prediction at bus stop with multiple routes

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.     

地质构造和地形对N.Barton岩质评定系数Q值的影响分析

挪威岩土所Barton等人给出的岩质评定系数Q值计算式,从岩体的完整性、结构面发育程度、地下水和地应力的影响等方面,全面地反映了岩体的工程地质状况和水文地质状况,定量地反映了围岩质量的好坏,但对宏观地质构造及地形地貌的影响考虑相对较少.文章通过理论分析和数值计算,提出了三个修正系数,即褶皱影响系数Kfo、断层影响系数Kfa以及地形影响系数Ktf,并针对具体工程给出了相应的取值范围,对Q值进行了修正,反应了工程地质构造、地形地貌对Q值的影响程度,使其表达更为准确.     

舰船气泡尾流特性的数值模拟和实验研究

文章基于N-S方程,并结合两相流的相关模型,数值模拟了双桨实船气泡尾流。通过海水中气体含量的变化情况,分析了尾流气泡产生、增大、破碎和消失等过程。得到了近场扩散区和远场尾流区中含气量的空间、时间分布特性。研究结果表明,近场尾流区中海水含气量基本以乘幂格式衰减,而在远场尾流区,则与指数衰减格式符合得较好。用光学的方法模拟测量了舰船尾流的衰减规律,实验结果与文中的理论数值结果一致。     

车辆荷载作用下连续梁桥动力响应仿真分析

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。     

船舶NAKAKITA燃油黏度控制系统故障诊断处理

针对NAKAKITA燃油黏度控制系统故障,阐述故障诊断处理方法,具体分析了实船中燃油黏度显示值偏离真实值的原因,为快速排除故障总结了故障诊断排除流程图,并提出了相应的预防建议。