南浦溪特大桥钢管拱肋悬拼阶段拱脚约束方式研究

南浦溪特大桥主桥为主跨258 m的钢管混凝土桁架上承式拱桥,拱肋为等截面钢管混凝土桁架结构,2道拱肋间距17.0 m,拱肋间布置13道横撑,单片拱肋由4根φ1200 mm×22 mm主钢管、水平向缀板和竖向腹杆组成.拱肋在工厂分段、分部件加工预拼合格后,运至现场拼装成吊装节段,采用缆索吊装斜拉扣挂法进行悬臂拼装.拱肋吊...     

山区县合流制污水管网排查重点部位研究

为落实住建部关于污水系统提质增效、黑臭水体治理等相关文件和指南的要求,针对山区县合流制污水管网排查工作中面临的系统复杂、底数不清的问题,通过对基础资料、现场踏勘、建设维护单位反馈、系统分析、风险预判以及管网的拟改造方案等前期工作的分析和研究,列举了污水管网排查在测量复核、缺陷检测、水质水量分析等三个阶段中,需要重点识别的排查部位,提高了管网排查的工作效率和后期实施提质增效方案措施的精准性。     

合流制箱涵末端水质净化站设计及运行分析

随着国家长江大保护行动计划的实施,长江流域一级支流黑臭水体河道亟需进行整治,以实现“清水绿岸,鱼翔浅底”的治理目标。重庆市花溪河为长江的一级支流,在其综合整治项目中,针对合流制箱涵溢流污染,提出了“源头雨污分流改造+末端新建水质净化站”的治理思路。其中土桥水质净化站的设计规模为1.0万m3/d,调蓄池设计容积为11 000 m3,出水执行准Ⅳ类标准。建成后的运行结果表明：在土桥箱涵排出的合流污水水量不同的工况下,通过采取末端截流、调蓄、净化等治理措施,土桥水质净化站运行正常,所排污水均达到排放标准并外排至花溪河。     

导弹中末段制导导引律转换研究

文章研究了导弹由中间段制导律向末段制导律转换的问题,提出了一种可变导引系数的组合制导律,即可变系数组合比例导引律（ACNG）。仿真结果表明,该导引律总体优于纯比例导引和固定系数组合比例导引律,中末段弹道过渡平滑,衔接良好,有利于中末段的交班,同时降低了导弹的需用过载。     

基坑变形组合预测分析及安全性评价

为提高基坑变形预测精度及合理评价基坑所处的安全状态,提出以支持向量机、极限学习机和GM(1,1)模型为单项预测模型,构建定权法、非定权法确定组合权值的组合预测模型,并利用累计变形量与变形控制值构建基坑变形的安全性评价指标,以判断基坑所处的安全状态,且采用重标极差法分析基坑安全性的发展趋势。实例分析表明： 1）组合预测较单项预测具有更高的预测精度,且能有效降低预测风险,增加预测结果的稳定性; 2）非定权组合的预测精度要略优于定权组合的预测精度,且以BP神经网络权值法的组合效果最优; 3）通过对某基坑的安全性分析,得知该基坑处于危险阶段,需采取必要的安全措施,且预测结果与安全分析结果一致,验证了预测方法和安全性评价方法2种分析方法的有效性和准确性。     

考虑合乘的共享自动驾驶汽车选择行为实证分析

共享自动驾驶汽车(Shared Autonomous Vehicles,SAV)是自动驾驶汽车和共享经济相结合的产物,为人们提供了一种新型的出行方式. 为探究出行者在考虑合乘的SAV与私家车或公共交通之间的选择偏好,实施了SAV选择意愿调查,并分析了考虑合乘的SAV的潜在用户特征. 基于问卷调查所得有效数据,采用K-Means 聚类法划分了历史出行模式,利用因子分析对性格态度特征进行了分类. 分别对有无私家车人群建立解释变量的参数服从不同分布的混合Logit 模型,并对参数标定结果进行对比分析. 研究结果表明,出行方式特性非常显著地影响出行者方式选择行为,性格态度特征是影响出行者选择考虑合乘的SAV出行方式的显著因素,且其显著性明显高于性别、年龄等社会经济属性.     

Atmospheric impact of ship traffic in four Adriatic-Ionian port-cities: Comparison and harmonization of different approaches

Shipping is a growing transport sector representing a relevant share of atmospheric pollutant emissions at global scale. In the Mediterranean Sea, shipping affects air quality of coastal urban areas with potential hazardous effects on both human health and climate. The high number of different approaches for investigating this aspect limits the comparability of results. Furthermore, limited information regarding the inter-annual trends of shipping impacts is available. In this work, an approach integrating emission inventory, numerical modelling (WRF-CAMx modelling system), and experimental measurements at high and low temporal resolution is used to investigate air quality shipping impact in the Adriatic/Ionian area focusing on four port-cities: Brindisi and Venice (Italy), Patras (Greece), and Rijeka (Croatia). Results showed shipping emissions of particulate matter (PM) and NOx comparable to road traffic emissions at all port-cities, with larger contributions to local SO₂ emissions. Contributions to PM_2.5 ranged between 0.5% (Rijeka) and 7.4% (Brindisi), those to PM₁₀ were between 0.3% (Rijeka) and 5.8% (Brindisi). Contributions to particle number concentration (PNC) showed an impact 2–4 times larger with respect to that on mass concentrations. Shipping impact on gaseous pollutants are larger than those to PM. The contribution to total polycyclic aromatic hydrocarbon (PAHs) concentrations was 82% in Venice and 56% in Brindisi, with a different partition gas-particle because of different meteorological conditions. The inter-annual trends analysis showed the primary contribution to PM concentrations decreasing, due to the implementation of the European legislation on the use of low-sulphur content fuels. This effect was not present on other pollutants like PAHs.     

Impact of weather on urban transit ridership

Utilizing daily ridership data, literature has shown that adverse weather conditions have a negative impact on transit ridership and in turn, result in revenue loss for the transit agencies. This paper extends this discussion by using more detailed hourly ridership data to model the weather effects. For this purpose, the daily and hourly subway ridership from New York City Transit for the years 2010–2011 is utilized. The paper compares the weather impacts on ridership based on day of week and time of day combinations and further demonstrates that the weather’s impact on transit ridership varies based on the time period and location. The separation of ridership models based on time of day provides a deeper understanding of the relationship between trip purpose and weather for transit riders. The paper investigates the role of station characteristics such as weather protection, accessibility, proximity and the connecting bus services by developing models based on station types. The findings indicate substantial differences in the extent to which the daily and hourly models and the individual weather elements are able to explain the ridership variability and travel behavior of transit riders. By utilizing the time of day and station based models, the paper demonstrates the potential sources of weather impact on transit infrastructure, transit service and trip characteristics. The results suggest the development of specific policy measures which can help the transit agencies to mitigate the ridership differences due to adverse weather conditions.     

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.     

A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation

This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling unit and its location, while also mimicking the search process that underlies housing decisions. In particular, we model a complete range of dwelling unit choices that include tenure type (rent or own), housing type (single family detached, single family attached, or apartment complex), number of bedrooms, number of bathrooms, number of storeys (one or multiple), square footage of the house, lot size, housing costs, density of residential neighborhood, and commute distance. Bhat’s (2015) generalized heterogeneous data model (GHDM) system is used to accommodate the different types of dependent outcomes associated with housing choices, while capturing jointness caused by unobserved factors. The proposed analytic framework is applied to study housing choices using data derived from the 2009 American Housing Survey (AHS), sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The results confirm the jointness in housing choices, and indicate the superiority of a choice set formation model relative to a model that assumes the availability of all dwelling unit alternatives in the choice set.