霞浦隧道穿过富水破碎F1断层的综合治理措施

霞浦隧道F1断层富水而破碎,施工中发生塌方和涌水的可能性很大。对此采用超前地质探查、超前预注浆阻水、加设大管棚、加强初期支护、加固圈3m全断面径向注浆、加强防、排水等一系列综合治理措施,成功地通过了F1断层。施工经验可供同类工程参考。     

基于MCD-AHP的预装式变电站城市化属性指标评价

为提高城市轨道交通预装式变电站建设的城市化属性效果,需要对其城市化属性指标做出准确评价. 首先,构建了包含经济属性、社会属性、节能属性3类指标的预装式变电站城市化属性指标评价体系,并给出了评价指标的量化模型;然后,引入最小协方差行列式 （minimum covariance determinant,MCD）稳健分析法对层次分析法 （analytic hierarchy process,AHP）中专家组所给出的判断矩阵进行检测,去除由于专家组成员之间主观认知的差异性而产生的离群矩阵;最后,应用改进后的层次分析法（MCD-AHP）对预装式变电站城市化属性指标权重进行计算并评价. 结果表明:去除离群矩阵样本后,上、下限矩阵计算所得指标权重差值的绝对值为0～0.0103、相对变化比率为0～9.31%,明显优于全样本状态下权重差值的绝对值率0.0138～0.1355和相对变化比15.99%～106.23%,该方法可有效提高判断矩阵的逻辑一致性和权重值的聚合性.      

细菌自愈合技术在混凝土修复中的应用

文章介绍了混凝土自愈合中使用的细菌类型以及它作为愈合剂的应用方式, 简要综述了随着细菌的增加混凝土各性能的变化。 混凝土中有微裂纹存在, 微裂纹会导致有害物质进入混凝土, 导致结构劣化。 为了避免微裂纹的不良影响, 可以采用自修复技术。 通过添加脲酶产生的细菌和钙源, 可以使方解石在混凝土中沉淀, 生物矿化技术能够封闭混凝土中的微裂缝。 新生成的微裂缝可以通过混凝土中的水合作用来密封, 如通过可以产生尿素的巴斯德芽孢杆菌, 枯草芽孢杆菌的尿素分解细菌与钙源一起整合, 使碳酸钙沉淀来封闭混凝土中的微裂缝。 优化细菌浓度可以更好的改善混凝土中的孔隙结构。 相关研究表明, 封装方法比直接施用方法具有更好的效果, 并且表明使用细菌可以提高混凝土的强度和耐久性。     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.     

Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.     

Impacts of urban built environment on empty taxi trips using limited geolocation data

This study identifies the determinants of the empty taxi trip duration (ETTD) by combining three high-resolution databases—geolocation data in New York City, geodatabase of urban planning data, and transportation facilities data. Considering the nature of duration data, hazard-based duration model is proposed to explore the relationships between causal factors and ETTD, coupling with three variations of baseline hazard distribution, i.e., Weibull distribution with heterogeneity, Weibull distribution, and log-logistic. Furthermore, the likelihood ratio test is presented to implement comparisons of three baseline hazard distributions, as well as spatial and temporal transferability of causal factors. The results show significant complementary effects by subway system and competitive effects by city bus and bicycling system, as well as significant impacts of trip length, airport trip, average annual income, and employment rate. Urban built environment, for instance, density of road, public facilities, and recreational sites and ratio of green space, has various impacts on ETTD. The elasticity estimations confirm significant spatial and temporal heterogeneity in impacts on ETTD. In addition, the analysis on elasticity also reveals the considerable impacts of severe traffic congestion on ETTD within Manhattan. The modeling can assist stakeholders in understanding empty taxi movements and measuring taxi system efficiency in urban areas.     

港口门机司机绩效考核系统设计

针对某港口企业下属门机队现行绩效考核和管理相对落后的状况提出了有效的改进策略。基于门机作业实时记录,采用Microsoft SQL2005数据库、VC＋＋6．0和水晶报表技术设计了一套司机作业绩效考核软件,实现了对现场采集数据的转换和存储,并结合作业票数据考核门机司机的作业量,自动生成每月的机械队员工工资报表,同时,港口企业通过司机绩效考核软件完成对生产数据的科学、有效的管理。     

A multiobjective land development optimization model: the case of New Castle County,Delaware

This paper develops a multiobjective optimization model to consider transportation impacts of the future development of land. The output of the model is the best location and type of land use that has minimal negative transportation effects and uses the maximum available public transportation infrastructure. It provides tools for both planners and transportation engineers and enables them to consider different scenarios of possible policies and land development. Since multiple objectives and their nonlinear structures are considered, the model is solved using mixed integer nonlinear programming. The final results are shown in both tabular and graphical format. The effectiveness of the model is applied to the northern part of New Castle County, Delaware. The results show that the model successfully finds the best locations for both residential and commercial land uses in order to meet several criteria discussed in the paper.