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The problems of TBM construction data loss, information barriers and absence of data mining have con⁃ strained the advancement of basic technologies in TBM field. Aimed at building a cloud computation platform for TBM operation information, a new concept of 3B, i. e. Born by digit, Born in format and Born to the cloud, was pre⁃ sented, the issues of information acquisition, transmission and storage during TBM operation were solved, and massive heterogeneous information intelligent transmission system and big data warehouse of TBM group were established. A structure model of cloud computation platform was designed by taking Hadoop system as ecosphere, and a cloud computation platform was built to deploy related algorithm, realizing on-line monitoring and data sharing, further⁃ more the data law mining of interaction of rock mass versus TBM machine was conducted based on big data tech⁃ nique, exploring the development direction of the information platform. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved. 相似文献
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In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort. 相似文献
105.
天然气是一种优质的替代燃料,具有污染小、安全系数高、运行费用低等优点。天然气已经成为城市公共交通领域应用最为成功和广泛的车辆替代燃料技术,为推动交通运输行业的节能减排做出了显著的贡献。液化天然气汽车,作为天然气汽车的一种类型,与传统汽柴油车相比,液化天然气汽车安装了包括液化天然气气瓶、气管路及各种控制阀门和仪表在内的专用装置,在对液化天然气汽车进行日常检查时需要针对液化天然气汽车的专用装置进行重点检查。本文则针对液化天然气汽车的特点,对液化天然气汽车的正确使用方法、日常检查方法及维护技术要求、以及相关注意事项三个方面进行了解读,为指导液化天然气汽车进行日常检查与定期维护提供了技术参考。 相似文献
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Katharina ParryMartin L. Hazelton 《Transportation Research Part B: Methodological》2012,46(1):175-188
Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester. 相似文献
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Stefanie Peer Carl C. Koopmans 《Transportation Research Part A: Policy and Practice》2012,46(1):79-90
Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation. 相似文献
108.
Dongjoo Park Laurence R. Rilett Byron J. Gajewski Clifford H. Spiegelman Changho Choi 《Transportation》2009,36(1):77-95
With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers
have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current
conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal
aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route
travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology
explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed
for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation
size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor,
(2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel
time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston,
Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It
was found that the optimal aggregation size is a function of the application and traffic condition.
相似文献
Changho ChoiEmail: |
109.
针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题,利用深度学习可以弥补其不足。首先,简略介绍了基于模型的里程计的研究现状,然后对比了常用的智能车数据集,将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种,从网络结构、输入和输出特征、鲁棒性等方面进行分析,最后,讨论了基于深度学习的智能车辆视觉里程计研究热点,从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。 相似文献
110.