螺旋桨水动力性能研究进展

升力面理论的应用日趋完善,面元法和N-S方程的方法已逐渐成为螺旋桨设计与水动力预报的主流,特别是能提供桨叶表面流动精细描述的CFD方法。虽然运用粘性流预报螺旋桨水动力性能的CFD方法较基于势流理论的升力线、升力面和面元法表现出较强的优越性,但是势流理论的完善性使其仍是螺旋桨设计和计算中最常用的工具。本文较全面地介绍了国内外螺旋桨水动力性能研究的最新进展,为螺旋桨相关研究提供参考。     

纳米晶Ni-Mo合金电沉积工艺

采用电沉积法制备Ni-Mo合金电极,通过测定电极在30%KOH溶液中的阴极极化曲线,研究不同沉积条件(钼盐浓度、pH值、沉积电流密度、沉积温度)对镀层析氢性能的影响,并用XRD、SEM对镀层进行了表征。结果表明:所获得的镀层为纳米晶结构,镀层表面颗粒分布均匀且具有很大的比表面积。通过正交实验确定了最佳电沉积工艺条件:钼盐浓度32 g/L,pH值8.0,沉积电流密度22 A.dm-2,温度50℃。最佳工艺条件制备出的Ni-Mo合金电极的析氢过电位仅为68 mV,比纯Ni电极降低348 mV。     

基于狄利克雷过程混合模型的城市活动聚类方法研究

手机信令数据不仅记录个体出行轨迹，也为分析城市活动空间分布特征提供了基础.本文提出一种基于狄利克雷混合模型的城市活动特征聚类方法，以手机信令提取居民出行OD为基础，将每个基站的到发出行量作为表征该基站所处空间位置的活动特征，研究特征的聚类方法.引入狄利克雷分布作为先验分布，由中餐馆模型推定特征聚类数量.与其他聚类方法相比，该方法最大的优点在于无需事先指定聚类数量，避免了传统聚类方法的缺陷.将本文方法应用到三亚市城市活动特征聚类当中，结果能够有效地反应不同城市功能组团的活动特征.     

考虑环保意识和出行习惯的公交出行选择行为模型

为解决公交出行选择行为、环保意识与出行习惯之间缺少定量分析交互作用的问题，基于计划行为理论(TPB)，结合习惯驱动行为理论，构建公交出行选择行为结构方程基本模型A，增加环保意识的扩展模型B，以及涵盖环保意识和出行习惯的扩展模型C，对比分析公交出行选择行为各影响因素之间的定量关系.最后，将模型运用于重庆市实例分析，共回收有效问卷401份.结果表明：模型C的拟合效果及解释性最好，模型B其次；环保意识、出行习惯均对公交出行方式选择行为存在影响，但影响程度低于行为意向和知觉行为控制.     

Traffic dynamics in a bi-modal transportation network with information provision and adaptive transit services

This paper has two major components. The first one is the day-to-day evolution of travelers’ mode and route choices in a bi-modal transportation system where traffic information (predicted travel cost) is available to travelers. The second one is a public transit operator adjusting or adapting its service over time (from period to period) based on observed system conditions. Particularly, we consider that on each day both travelers’ past travel experiences and the predicted travel cost (based on information provision) can affect travelers’ perceptions of different modes and routes, and thus affect their mode choice and/or route choice accordingly. This evolution process from day to day is formulated by a discrete dynamical model. The properties of such a dynamical model are then analyzed, including the existence, uniqueness and stability of the fixed point. Most importantly, we show that the predicted travel cost based on information provision may help stabilize the dynamical system even if it is not fully accurate. Given the day-to-day traffic evolution, we then model an adaptive transit operator who can adjust frequency and fare for public transit from period to period (each period contains a certain number of days). The adaptive frequency and fare in one period are determined from the realized transit demands and transit profits of the previous periods, which is to achieve a (locally) maximum transit profit. The day-to-day and period-to-period models and their properties are also illustrated by numerical experiments.     

Adding temporal information to direct-demand models: Hourly estimation of bicycle and pedestrian traffic in Blacksburg,VA

Cycling and walking are environmentally-friendly transport modes, providing alternatives to automobility. However, exposure to hazards (e.g., crashes) may influence the choice to walk or cycle for risk-averse populations, minimizing non-motorized travel as an alternative to driving. Most models to estimate non-motorized traffic volumes (and subsequently hazard exposure) are based on specific time periods (e.g., peak-hour) or long-term averages (e.g., Annual Average Daily Traffic), which do not allow for estimating hazard exposure by time of day. We calculated Annual Average Hourly Traffic estimates of bicycles and pedestrians from a comprehensive traffic monitoring campaign in a small university town (Blacksburg, VA) to develop hourly direct-demand models that account for both spatial (e.g., land use, transportation) and temporal (i.e., time of day) factors. We developed two types of models: (1) hour-specific models (i.e., one model for each hour of the day) and (2) a single spatiotemporal model that directly incorporates temporal variables. Our model results were reasonable (adj-R² for the hour-specific [spatiotemporal] bicycle model: ∼0.47 [0.49]; pedestrian model: ∼0.69 [0.72]). We found correlation among non-motorized traffic, land use (e.g., population density), and transportation (e.g., on-street facility) variables. Temporal variables had a similar magnitude of correlation as the spatial variables. We produced spatial estimates that vary by time of day to illustrate spatiotemporal traffic patterns for the entire network. Our temporally-resolved models could be used to assess exposure to hazards (e.g. air pollution, crashes) or locate safety-related infrastructure (e.g., striping, lights) based on targeted time periods (e.g., peak-hour, nighttime) that temporally averaged estimates cannot.     

Bus travel time prediction using a time-space discretization approach

The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.     

大数据背景下的交通模型发展思考

将传统交通调查和模型与新的信息技术相结合,根据大数据的特征改进交通模型的方法体系,是交通模型发展面临的极大挑战.分析交通大数据对交通模型研发的促进作用,特别是在人口及就业岗位分布、综合交通网络数据和基于交通调查的模型参数等核心数据方面的支撑作用.从相关定义、市场细分以及信息完整性三个方面阐述大数据与交通模型的一致性问题.深入讨论应用大数据改进交通调查技术、4D模型研发、出行分布目的地选择模型、轨道交通出行接驳模型、出行成本校核等交通模型关键技术.通过对比大数据和交通模型的决策分析过程,指出大数据与交通模型互动发展的途径.     

Assessing the impact of the National Cycle Network and physical activity lifestyle on cycling behaviour in England

This paper examines the association between access to National Cycle Network (NCN) routes in England and an individual’s cycling behaviour whilst accounting for their broader physical activity lifestyle and controlling for their socio-economic circumstances. It identifies a positive association between access to these routes and the total minutes of any form of cycling, and the number of days that cycling takes place primarily for recreational purposes. The broader physical activity of individuals also has a positive association with cycling. Walking appears most likely to be complementary to non-recreational cycling, whilst participation in sport with all forms of cycling, but not with longer duration utilitarian trips. The research also indicates that access to NCN routes has the potential to increase such cycling further, with the exception of longer utilitarian trips, as does a more physically active lifestyle, particularly walking. The main policy implications of the research are to recognise that cycling is intrinsically linked to other physical activity, notably, walking, but that the NCN routes measured in this study primarily support longer duration recreational activity, which is also affected by sporting activity. This suggests that one avenue for achieving the health benefits of cycling may be through promoting NCN routes to harness a more generally active lifestyle and particularly in leisure, whilst sustainability may be further promoted through being linked more to other active travel such as walking. There is a therefore a need to exploit the potential of such NCN route provision as part of this promotion.