A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.     

Simulating ad-hoc demand-responsive transportation: a comparison of three approaches

Transport models are used to evaluate new infrastructure and public transport services, varied levels of demand, and new ideas for demand management. Exploring these proposals virtually is easier than implementation and testing in situ. However, existing models are based around traditional forms of transportation. As part of a feature analysis using a case study approach, three different simulation packages (a simple custom-developed package, traffic microsimulation, and agent-based simulation) are used to develop and demonstrate simulations of demand-responsive transportation (DRT) and analyze the advantages and disadvantages of each simulation approach for evaluating DRT. While the simulations display some relational replication (meaning they produce similar relational patterns with respect to certain variables), they do not show distributional replication (that is, the value of the results is not statistically similar), meaning that under- or over-estimation of predicted travel could occur. Recommendations for the application of each modeling approach are made.     

The Bid-rent Land Use Model of the simple,efficient, elegant,and effective model of land use and transportation

Integrated land use/transportation forecasting models add significant policy and infrastructure alternatives analysis capabilities to the urban planning process. The financial, time, and staff requirements to develop these models has put them beyond the reach of most small to medium sized urban areas. This paper presents the land use allocation submodel of the Simple, Efficient, Elegant, and Effective model of land use and transportation (SE³M), an integrated land use and transportation forecasting model founded upon Economic Base Theory and Bid-rent Theory. The Bid-rent Land Use Model (BLUM) is an agent based, spatial competition model utilizing unique utility curves for willingness to pay and incomes for budget constrained abilities to pay for each agent. The model structure, estimation, calibration, implementation, and validation are presented. With a single year of land use data available, the validation approach used the Kappa Index of Agreement to spatially check model outputs against base year control data while controlling for agreement by chance. The U.S. territory of Guam is used as the case study/proof of concept implementation for this model framework. Once calibrated, BLUM could solve the spatial competition problem on Guam in less than two minutes of processing time with over 90% accuracy.     

港口岸线环境补偿人工沙滩工程理论与实践

依托港口岸线建设环境补偿人工沙滩工程是践行国家生态文明建设战略,实现港、城一体化发展的有益举措。针对人工沙滩建设中的理论和关键技术问题,通过自主研发,取得以下主要创新性成果:1)发展了沙、泥并存下沙滩动力地貌学基础理论;2)形成先进的数值、物理模拟技术;3)提出适用于各种复杂泥沙环境下的人工沙滩设计原则;4)研发了人工沙滩防侵蚀、防泥化的整治与养护措施。研究成果已成功应用于不同复杂泥沙环境下的实际沙滩工程中,实现了良好的经济、社会和生态环境效益,具有广阔的推广应用前景。     

游艇码头定位桩内力分析

定位桩是游艇码头的一种重要锚碇形式,现行规范对游艇码头风荷载、波浪荷载等规定存在不详尽之处,为完善游艇码头定位桩的计算分析,采用ANSYS有限元软件对游艇码头在风荷载和波浪荷载作用下的受力情况进行分析,对游艇码头定位桩计算提出合理建议,建议游艇码头计算采用整体建模,风荷载的遮挡效应应考虑船型、受风高度、船舶间距等因素。     

BIM技术在深圳机场三跑道填海工程设计中的应用

针对BIM技术在填海工程设计中的应用,深圳机场三跑道填海工程设计中采用AUTODESK平台,以基于云端的协同平台进行协同设计,将各专业更加紧密地联系在一起,解决了传统设计的沟通、协调难题,且根据勘察资料创建三维地质模型,并采用Civil 3D软件进行基槽和海堤施工图设计,提高设计效率及精度,为BIM技术在水运行业的推广及应用提供借鉴。     

冰载荷下的船舶运动建模

随着极地航道的开辟以及极地海洋能源开发的需要,海冰与船体的相互作用以及极地船舶航行与作业安全越来越受到关注。本文主要针对破冰过程的几个典型阶段,对冰载荷对船舶的作用力进行了分析与计算,并考察了破冰船在不同的海冰密集度、冰厚以及船舶不同航速情况下的不同影响。     

基于粗糙-模糊软计算的船板线加热成形加工参数预报方法

提出一种基于粗糙-模糊软计算的建模方法,构造一种基于贴近方向修正的高精度模糊推理算法,通过有限元分析(FEA)建立板材线加热成形工艺信息系统,根据粗糙集软计算获取线加热成形的工艺规律,运用模糊推理实现加工参数的快速准确预报。粗糙-模糊建模适用于线加热成形过程的静态或动态模型的构建,为实现该工艺的智能建模与控制提供一种新的技术手段。