基于聚类站点客流公共特征的轨道交通车站精细分类

已有城市轨道交通车站分类多基于定性分析，不能满足精细化设计和运营的需要。本文提出一种基于聚类站点公共特征的站点精细分类方法。首先，将来源于AFC(Automatic Fare Collection)的进站客流量数据处理为时间序列数据，并基于K-Means++算法对各个站点的客流量进行聚类；其次，建立客流量聚类结果与土地利用特征多维参数的拟合方程，计算获得居住密集型、工作就业型以及区域中心型等5种大类站点的客流量公共特征。在此基础上，充分考虑属于同一大类站点不同站点的细分特性，使用5类客流量公共特征比重组合精细描述具体站点类型。 实例结果表明，使用本文提出的精细分类方法计算得到的每个站的客流量拟合值与真实客流值间的平均绝对百分比误差控制在14%以内，说明该分类方法具有可行性。     

Integrated models of land use and transportation for the autonomous vehicle revolution

ABSTRACT

The advent of the autonomous vehicle (AV) will affect not only the transportation system, but also future patterns of land development. Integrated land use and transportation models will be critical tools in assessing the path forward with this technology. Key questions with respect to land use impacts of AVs arise from potential changes in sensitivity to travel and reduced demand for parking. It is an open question whether AVs will induce urban sprawl, or whether spatial economies of agglomeration will mitigate any reductions in travel time sensitivity. The deployment of shared fleets of AVs would likely reduce parking demand, producing yet to be explored impacts on property development within existing urban footprints. We perform a critical assessment of currently operational models and their ability to represent the adoption of AVs. We identify the representation of time in such models as a vital component requiring additional development to model this new technology. Existing model applications have focused on the discrete addition of new infrastructure or policy at a fixed point in time, whereas AV adoption will occur incrementally through time. Stated adaptation surveys are recommended as tools to quantify preferences and develop relevant model inputs. It is argued that existing models that assume comparatively static equilibrium have been convenient in the past, but are insufficient to model technology adoption. In contrast, dynamic model frameworks lack sufficient structure to maintain reasonability under large perturbations from base conditions. The ongoing advancement of computing has allowed models to move away from being mechanistic aggregate tools, towards behaviourally rich depictions of individual persons and firms. However, much work remains to move from projections of existing conditions into the future, to the evolution of the spatial economy as it evolves through time in response to new technologies and exogenous stresses. Principles from complex and evolutionary systems theory will be important in the development of models with the capacity to consider such dynamics.     

屏蔽盒的设计技巧

针对目前军用电子产品结构设计中，机箱屏蔽盒设汁的繁琐步骤，介绍了一种屏蔽盒设计的新方法，使用该方法，能够快速、准确地设计出满足屏蔽性能要求的屏蔽盒。     

国内外城市轨道站点区域联动 开发规划与实施案例分析

城市轨道站点与周边土地的联动开发是实现二者协调发展的关键因素之一,对城市经济和交通都具有积 极作用。对国内外城市轨道站点区域联动开发进行总结分析有助于指导中国轨道站点区域的联动开发。从产权和 城市经济学视角,将轨道站点联动开发分为内外联动和上下联动两类,并进行了相关界定。分别结合日本联动开 发政策以及涩谷站数据、上海联动开发政策和打浦桥站区域数据,对内外联动和上下联动的规划和实施途径及其 在中国的适用性进行了分析。最后,参考更多经典的联动开发案例,对比分析了国内外城市轨道站点区域联动开 发的实施机制及特征;并从政策和规划设计两个角度深入阐述对中国联动开发的启示。研究结果表明,中国可采 用“政府统筹、市场执行”的模式;根据中国土地所有制度,探索“集约换使用权”“整体出让使用权”等多样 化的土地管理模式,有利于成本分担和收益共享的反哺策略;制定配套且细化的技术规范,进行立体化、多样化、 一体化衔接设计。     

铁道信号系统安全保障研究

在信号系统的设计、制造、施工、调试等阶段进行安全保障很重要。重点介绍按照欧洲铁路安全标准对铁道信号系统进行交叉接收以及安全管理。     

矩形土压平衡式顶管在广州地铁工程中的应用

通过广州地铁6号线东湖站矩形土压式顶管法应用实例,介绍矩形土压平衡式顶管机构造特点、工艺原理、关键工序、控制要点、施工测量及监测等,并就顶管施工应注意的问题及预防措施提出建议。     

我国铁路能源利用效率和节能减排分析研究

铁路是我国综合运输体系的骨干,对社会经济的发展具有基础支撑作用。通过铁路、公路、民航和水运等交通运输方式能耗的比较,铁路单位换算周转量能耗较低（仅比水运高）;通过分析全路机车牵引综合能耗情况、以及往年内燃机车、电力机车能耗情况,说明了铁路牵引动力结构的变化使铁路能源利用效率得到了提高,发展铁路运输对于改善交通运输业的能源结构及能源使用效率将起到积极的推动作用;铁路借助其在能效方面的比较优势,通过替代其他运输方式以及自身能效的提高从而达到节能减排的目的,对实现我国低碳经济做出了重大的贡献。     

接地变压器在轨道交通电力系统中的应用与分析

介绍接地变压器的原理、工作特点,以及接地变压器在轨道交通供电系统中的实际运用。从保护动作可靠性、系统运行方式调整的灵活性和安全性以及建设成本3个方面,对2种不同接线方式的接地变压器进行了分析比较。并从日常运行维护角度建议接地变压器采用独立的断路器。     

Time windows in workers' activity patterns: Empirical evidence from the Netherlands

This paper assumes that activities at the home and work location are important determinants of individuals' paths through time and space. Fixed activities at these locations determine to a large extent the duration and timing of time windows – blocks of time available for participation in travel and out-of-home non-work activities. Taking the time spent at home and at the workplace as a starting point, this paper classifies activity patterns on workdays into six groups with distinct home- and work-stay patterns. For this, data are used from the 1998 Netherlands National Travel Survey. The six clusters vary in terms of the duration and timing of time windows and some of the differences can be explained by commute characteristics, types of non-work activities performed, workers' sociodemographic attributes, and their spatiotemporal environment. However, the impact of sociodemographic and spatiotemporal variables on cluster membership is shown to be weak.     

Spatiotemporal influence of land use and household properties on automobile travel demand

Understanding the patterns of automobile travel demand can help formulate policies to alleviate congestion and pollution. This study focuses on the influence of land use and household properties on automobile travel demand. Car license plate recognition (CLPR) data, point-of-interest (POI) data, and housing information data were utilized to obtain automobile travel demand along with the land use and household properties. A geographically and temporally weighted regression (GTWR) model was adopted to deal with both the spatial and temporal heterogeneity of travel demand. The spatial-temporal patterns of GTWR coefficients were analyzed. Also, comparative analyses were carried out between automobile and total person travel demand, and among travel demand of taxis, heavily-used private cars, and total automobiles. The results show that: (I) The GTWR model has significantly higher accuracy compared with the Ordinary Least Square (OLS) model and the Geographically Weighted Regression (GWR) model, which means the GTWR model can measure both the spatial and temporal heterogeneity with high precision; (II) The influence of built environment and household properties on automobile travel demand varies with space and time. In particular, the temporal distribution of regression coefficients shows significant peak phenomenon; and (III) Comparative analyses indicate that residents’ preference for automobiles over other travel modes varies with their travel purpose and destination. The above findings indicate that the proposed method can not only model spatial-temporal heterogeneous travel demand, but also provide a way to analyze the patterns of automobile travel demand.