Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

缓波型非粘接柔性立管的干涉敏感性

针对非粘接柔性立管在波浪和海流作用下易发生立管干涉的问题,以我国南海某油田输油立管和相邻锚链为例,分别对拖曳力系数、管道单位长度质量、外径、内部介质密度、立管抗拉弯扭刚度和悬挂角度等参数进行敏感性分析,得出不同参数的敏感性情况,并在此基础上提出一些建议供实际工程设计参考.     

隧道爆炸冲击波的危害及控制措施

目前山岭隧道施工基本以钻爆法为主,爆破过程中产生的冲击波会对隧道内的人员及机械设备安全产生较大影响,造成重大安全事故和财产损失。本文主要介绍了冲击波在隧道内传播会形成超压,且随着传播距离增加超压峰值逐渐衰减到大气压范围内;基于超压准则评估了冲击波对人体、隧道内设施及周边建筑物的危害程度;通过在隧道内安装简易的冲击波缓冲装置及加强监测、设置安全警戒线等控制措施,减弱了爆破冲击波的危害,从而达到安全快速施工的目的。     

25t轴重荷载作用下隧底脱空区域受力特性分析

为了研究在役铁路隧道在通车之后隧底脱空病害的问题,采用有限元理论,建立隧道脱空区域在围岩压力与25 t轴重列车动载作用下的数值计算模型,主要研究80 cm与40 cm脱空宽度分别距隧道中心线0,80 cm与160 cm时脱空区域的受力特性。结果表明:在围岩压力下,脱空区域中线上壁和外侧顶角混凝土中产生拉应力及内侧顶角中产生压应力,其中压应力对脱空的宽度更为敏感;同时施加列车动载作用时,脱空区域上壁出现了竖向动应力与横向拉应力,得到了脱空区域力学指标的最大响应值及其出现的具体位置,宽度的增加对脱空区上壁横向拉应力更为显著,上壁横向拉应力增幅超过200%,竖向动应力增幅达50%。因此,隧底脱空区周围应力分布复杂,拉应力与压应力在脱空区域同时存在,应力突变严重,对脱空现象应及时组织处理。     

高速铁路轨道的波阻抗及影响因素研究

高速铁路轨道在雷击或故障冲击电流作用下会产生暂态冲击过电压,该过电压的大小由轨道的波阻抗决定。通过建立高速铁路线路的仿真模型,分析直角冲击波在轨道上的传播与折反射过程,提出轨道的波阻抗的计算方法,研究钢轨类型、土壤电阻率、钢轨对地过渡电阻对波阻抗的影响。结果表明:P60型轨道波阻抗数值为235. 55Ω;轨道的波阻抗主要受钢轨类型和土壤电阻率的影响,高速铁路轨道的波阻抗在在210～250Ω变化;在高速铁路轨道仿真模型中,线路末端电阻等于波阻抗时,可以有效地消除折反射对仿真结果的影响,末端电阻的取值不需要考虑钢轨地过渡电阻的影响。研究给出高速铁路轨道波阻抗的范围及仿真模型中末端电阻的取值方法,可以为轨道过电压计算、分析与仿真提供理论与方法参考。     

冲压机械振动对高铁桥梁及行车动力影响分析

高速铁路桥梁的平顺性和稳定性对运营列车的平稳性和安全性有很大影响。为研究冲压机械产生的外部振动激励对高铁桥梁的影响,首先通过对此机械引起的地面振动进行实测,并结合有限元分析软件,确定最大冲击荷载作用下产生的地面振动及传播至桥墩处的振动;然后通过建立列车-轨道-桥梁耦合动力学模型,将桥墩处的地面振动作为激励输入,分析列车以不同速度通过时车辆、桥梁动力学响应。结果表明:地面冲击振动有限元模型计算结果与实测结果基本相符,验证了模型的可靠性;地面振动对桥梁响应会产生一定的影响,距振源50 m处地面振动对桥梁所产生的影响较距振源80 m处(桥墩处)的大,但对运行车辆的影响很小;随着车速由250 km/h至350 km/h,车辆及桥梁各结构的动态响应均有所增大,但都未超出安全限值。因此,冲压机械冲击作用导致的地面振动对列车-轨道-桥梁系统动态服役性能影响非常有限。     

Estimation of extreme significant wave heights and the associated uncertainties: A case study using NORA10 hindcast data for the Barents Sea

The reliability of an offshore structure is dependent on its response to the extreme wave climate; therefore, an adequate knowledge of the wave climate at a location is a prerequisite during design as well as in marine operations. This study aims to contribute to the knowledge of the extreme wave climate in the Norwegian Sector of the Barents Sea, using wave hindcast datasets from the Norwegian Reanalysis 10 km (NORA10) database for four locations.We have considered three commonly used methods for the estimation of extreme wave heights, that is, the initial distribution method, the peak over threshold method, and the annual maxima method. The parametric bootstrap concept is considered in the estimation of the epistemic uncertainty related to sample size. The estimated 100-year significant wave heights obtained from the three methods differ, and the degree of variability in the estimates varies, depending on the dataset. The epistemic uncertainty due to sample size is wider when considering the annual maxima method.The estimates obtained from the three methods show the importance of considering different methods and their associated uncertainties when estimating extreme wave values for design. While it is difficult to single out the best method among the three, the estimated values give knowledge of the possible range of the extreme significant wave heights at the locations. Generally, the datasets considered in this study suggest that the wave climate is less harsh further north compared to the southern region of the Barents Sea. The datasets do not suggest any temporal trends in the historical significant wave heights at any of the locations.     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.     

基于时延特性建模的多断面短时交通流预测

针对现有交通流预测方法未充分考虑多断面车流演变规律，提出基于时延特性建模的时空相关性计算方法. 该方法采用对不同断面、不同时刻交通流的分布相似性度量，对输入的车辆到达数据序列进行切割构建时空相似度矩阵，得到相邻断面之间的时延参数. 基于时延特性建模，将多断面之间的流量信息进行融合，使用长短时记忆(LSTM)网络进行流量预测. 通过对实际路段数据的预测和结果分析，验证所提方法的有效性和实用性.