Estimating bike-share trips using station level data

Bicycle sharing systems (BSS) have increased in number rapidly since 2007. The potential benefits of BSS, mainly sustainability, health and equity, have encouraged their adoption through support and promotion by mayors in Europe and North America alike. In most cases municipal governments desire their BSS to be successful and, with few exceptions, state them as being so. New technological improvements have dramatically simplified the use and enforcement of bicycle return, resulting in the widespread adoption of BSS. Unfortunately little evaluation of the effectiveness of differently distributed and managed BSS has taken place. Comparing BSS systems quantitatively is challenging due to the limited data made available. The metrics of success presented by municipalities are often too general or incomparable to others making relative evaluations of BSS success arduous. This paper presents multiple methodologies allowing the estimation of the number of daily trips, the most significant measure of BSS usage, based on data that is commonly available, the number of bicycles available at a station over time. Results provide model coefficients as well as trip count estimates for select cities. Of four spatial and temporal aggregate models the day level aggregation is found to be most effective for estimation. In addition to trip estimation this work provides a rigorous formalization of station level data and the ability to distinguish spatio-temporal rebalancing quantities as well as new characteristics of BSS station use.     

京张高铁GIS地理信息系统构建研究

为更好地服务于智能京张高铁建设,发挥空间地理信息的基础支撑作用,为京张高铁各业务信息系统提供规范的铁路地理信息数据和空间信息服务,实现铁路空间信息资源共享共用。面向铁路应用需求,运用面向对象的空间数据管理、服务式GIS、地图缓存及更新等关键技术,采用"一张图+N服务"的空间信息应用模式,完成铁路、地方多源空间信息的融合,提供京张高铁GIS门户、在线地图、数据管理等服务。京张高铁地理信息平台集成了国家基础地理信息数据、铁路专业公用地理信息数据,形成了面向铁路多业务、多模式的铁路地理信息平台标准体系框架。     

The influence of e-carsharing schemes on electric vehicle adoption and carbon emissions: An emerging economy study

Moving toward sustainable mobility, the sharing economy business model emerges as a prominent practice that can contribute to the transition to sustainability. Using a system dynamics modeling approach, this paper investigates the impacts of an e-carsharing scheme in carbon emissions and in electric vehicle adoption. We study the VAMO scheme located in Fortaleza, Brazil, as the first e-carsharing scheme in the country. We study two policies combined: a VAMO planned growth policy and a retirement policy for conventional vehicles. Our results show that the VAMO incentive policy is an important factor to reduce emissions and to increase awareness of electric vehicles, highlighting the role of the government as an institutional entrepreneur, stimulating and sustaining the VAMO scheme. The retirement policy in combination with the VAMO incentive policy obtained the best results in our simulations, reducing 29% of CO₂ emissions and increasing 36% electric vehicle adoption, when compared to the business-as-usual scenario. The main conclusions are that such e-carsharing schemes offer direct and indirect benefits to urban mobility (specially to electric vehicle adoption) and that they depend on how the government supports them.     

CBTC系统ZC产品工程数据校核研究

根据CBTC系统ZC产品数据配置及测试过程中遇到的问题,结合目前ZC产品特点与CBTC系统演进趋势,从提高数据测试质量角度提出解决方案。     

驾驶压力影响因素与识别方法研究综述

高强度的驾驶压力会对驾驶人的情绪、决策及行为产生负面影响，可能导致交通事故，并对驾驶人的健康状况造成长期影响。本文借助CiteSpace软件对驾驶压力研究进行可视化分析，进一步从驾驶人自身因素、车辆内部和外部因素这3方面总结驾驶压力影响因素，并归纳整理驾驶压力识别方法。总结发现：交通拥堵、道路复杂性及新技术使用等驾驶环境因素是引发或增加驾驶压力的主要因素；非职业驾驶人易受车辆外部环境的影响，职业驾驶人易因工作产生负面情绪，导致驾驶压力增加。驾驶压力识别主要基于主观测评量表、驾驶行为分析及生理数据分析等方法开展研究，其中，基于生理数据的识别方法因其较高的识别精度和准确度被认为在驾驶压力识别领域具有明显的优势。从研究趋势来看，未来研究需重视社会环境以及多因素叠加对驾驶压力的影响，特别关注职业驾驶人及新技术的影响，以及如何采用多模态数据融合方法实现实时监测，以提高驾驶压力识别的精度。     

基于秩最小化矩阵去噪的船舶轨迹重构方法

针对船舶自动识别系统(Automatic Identification System，AIS)在实际应用中存在错误数据频发、数据丢包等问题，本文提出一种基于秩最小化矩阵去噪的船舶轨迹重构方法，利用去噪实现轨迹重构，同时，实现对轨迹的去噪和缺失补全。该方法通过线性插值实现经度对齐，将轨迹数据转化为轨迹矩阵，从而补全轨迹中的缺失值。由于补全结果存在非常大的误差，因此，引入 PLR(Patch-Based Low-Rank Minimization)算法去噪，消除误差。同时，为进一步提升补全效果，通 过2D-VMD(Two-Dimensional Variational Mode Decomposition)算法将矩阵分解为不同频率的IMF (Intrinsic Mode Function)，并分别进行PLR去噪，合并去噪结果，得到最终重构后轨迹。本文以长江武汉段水域船舶AIS轨迹为研究对象，通过实验证明该方法在不同缺失比例以及随机缺失和连续缺失两种情境下具有鲁棒性和较强的稳定性；并与 HALRTC(High-Accuracy Low-Rank Tensor Completion)、TRMF(Temporal Regularized Matrix Factorization)等方法进行比较，结果表明， 该方法相较于HALRTC等方法具有更高的精度，并在高损失率下表现出较好的重构效果。     

Association rule learning to improve deficiency inspection in port state control

ABSTRACT

The inspection of foreign ships in national ports is a critical measure in port state control (PSC), preventing substandard ships from entering national ports. Multifarious inspection items, limited inspection time and inspector manpower are challenging PSC inspection. This research applies data mining to analyze historical PSC inspection records in Taiwan’s major ports to extract potential valuable information for PSC onboard inspections. Using the Apriori Algorithm, the analysis identifies many useful association rules among PSC deficiencies in terms of specific ship characteristics, such as ship types, societies, and flags. The general rules identified show that the items ‘Water/Weathertight conditions’ and ‘Fire safety’ are significantly related. Besides, in the analysis of the various ship types, several different rules are found. After comparing the analysis of ship types and ship societies, it can be observed that the association rules for specific ship types, such as oil tankers, have a better effect than those for individual ship societies do. These identified rules can not only help inspectors effectively spot the associated deficiencies, but also improve the efficiency of PSC inspection. The ports other than Taiwan’s ports can apply a similar analysis method to identify corresponding association rules suitable for their own inspections.     

Urban road traffic noise spatiotemporal distribution mapping using multisource data

Many residents are disturbed by road traffic noise which needs to be controlled and managed. The noise map is a helpful and important tool for noise management and acoustical planning in urban areas. However, the static noise map is not sufficient for evaluating noise annoyance at different temporal periods. It is necessary to develop the dynamic noise map or the noise spatiotemporal distribution. In this study, a method about urban road traffic noise spatiotemporal distribution mapping is proposed to obtain the representative road traffic noise maps of different periods. This method relies on the proposed noise spatiotemporal distribution model with two time-dependent variables - traffic density and traffic speed, and the spatiotemporal characteristics derived from multisource data. There are three steps in the method. First, the urban road traffic noise spatiotemporal distribution model is derived from the law of sound propagation. Then, the temporal characteristics are extracted from traffic flow detecting data and E-map road segment speed data by the outlier detection analysis. Finally, the noise distributions corresponding to different periods are calculated by an efficient algorithm which can save 90% above of the computing time. Moreover, a validation experiment was conducted to evaluate the accuracy of the proposed method. There is only 2.26-dB[A] mean absolute error that is within an acceptable range, which shows that the method is effective.     

基于大数据的智能监测电务故障应急管理研究

大数据在突发事件应急管理中的应用,不仅能够改善政府管理者的管理思维,提高决策理性,还能提高应急管理的处置效率,提高预测预警能力。为加强信号设备故障处理流程管理,充分发挥设备故障应急抢修的积极作用,做好信号设备故障后的应急处理工作,将故障延时压缩到最短,显得尤为重要。重点介绍基于大数据的智能监测标准化电务故障应急指挥管理办法的实现及应用。     

Research and design of ship-shore integrated engine room intelligent operation and maintenance system based on data brain北大核心CSCD

[目的]为提升舰船机舱运维的智能化水平,针对机舱设备种类多、数量大、耦合关系复杂等特点,梳理机舱运维智能化应用的技术路线,开发船岸一体化机舱智能运维系统。[方法]首先,分析国内外机舱智能运维技术的发展趋势,融合利用健康管理、大数据挖掘、数字孪生、数据轻量化传输等技术优势;然后,设计符合舰船机舱特点的一体化智能运维系统。[结果]提出了一种基于数据大脑赋能的机舱智能运维系统功能框架、平台设计、运行体系、运行流程及关键技术应用。[结论]研究成果可为舰船机舱智能运维系统设计及应用实践提供参考。