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31.
高速公路路面养护管理系统开发和研究 总被引:6,自引:0,他引:6
在考虑当前路况的基础上,又考虑路龄的使用性能预测模型,预测的结果更为快速准确。探讨高等级公路道路养护管理决策系统和经济分析方法,提出了改进优劣系数法,该方法充分利用了专家经验知识,减小了决策误差。开发了高速公路路面养护管理辅助决策系统,建立高速公路基本数据库,由系统自动进行路面使用性能评价分析,并利用专家知识系统得出推荐养护方案,系统投入运行后反响良好。该养护管理系统的建立和实施进一步提高和改善了江苏省高等级公路的使用管理和养护管理模式,为高等级公路早日实现网级化系统管理奠定基础。 相似文献
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高速公路路线方案优选模型及其应用 总被引:8,自引:0,他引:8
利用灰色物元分析法,建立高速公路路线方案的优劣排序模型,对各路线方案进行多因素综合评价,为择优选择路线方案提供一种科学的评价方法。 相似文献
34.
通渝隧道围岩变形的神经网络预测 总被引:1,自引:0,他引:1
隧道新奥法施工中,常以围岩变形量作为评判围岩稳定性和支护结构经济合理性的重要指标。公路隧道围岩变形量是随时间而变化的数据序列,因而可以建立一些实时跟踪预测模型和方法。根据通渝隧道围岩拱顶下沉位移变形的特性,采用神经网络技术来预测其变形量,结果表明该方法简易、有效。 相似文献
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������ѩ�ڳ��е�·��ͨ��ͻ���¹�Ԥ�� 总被引:1,自引:0,他引:1
�����£��̹����������ԣ���¶¶ 《交通运输系统工程与信息》2016,16(6):216-221
37.
Transfer functions are often used together with a wave spectrum for analysis of wave–ship interactions, where one application addresses the prediction of wave-induced motions or other types of global responses. This paper presents a simple and practical method which can be used to tune the transfer function of such responses to facilitate improved prediction capability. The input to the method consists of a measured response, i.e. time series sequences from a given sensor, the 2D wave spectrum characterising the seaway in which the measurements are taken, and an initial estimate of the transfer function for the response in study. The paper presents results obtained using data from an in-service container ship. The 2D wave spectra are taken from the ERA5 database, while the transfer function is computed by a simple closed-form expression. The paper shows that the application of the tuned transfer function leads to predictions which are significantly improved compared to using the transfer function without tuning. 相似文献
38.
本文采用有限元软件ABAQUS建立了船舶撞击高桩码头群桩的空间有限元模型。通过计算评估了撞击力、桩体刚度、撞击位置和撞击角度下对群桩结构损伤位置的影响。基于人工神经网络(ANN)方法,对不同参数组合下的群桩结构损伤位置进行了预测,并对ANN方法的可行性进行了评估。 相似文献
39.
Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations. 相似文献
40.
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. 相似文献