Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

隧道工程建设地质预报及信息化技术的主要进展及发展方向

复杂的不良地质条件是制约隧道安全高效建设的主要因素,要实现隧道工程的安全高效建设,首先要提高地质预测预报技术水平及其信息化程度。1)介绍我国复杂不良地质隧道超前预报的方法进展及其应用,包括突水突泥灾害源超前探测方法与设备、断层破碎带超前预报、城市地铁溶洞和孤石等探测的进展及应用等;2)介绍我国隧道岩爆监测预警方法及其应用,预报清楚之后就要加强安全风险过程监控;3)介绍基于BIM技术的建筑物(隧道工程)安全风险监控最新进展,包括安全风险实时感知系统和实时预警系统;4)指出隧道工程建设信息化技术的发展方向,包括开展基于大数据技术的TBM/盾构施工的分析与控制研究以及数字隧道向智慧隧道(建设和运营维护)的发展。     

A data model applied to the knowledge-based ICAS

Image-guided computer aided surgery system (ICAS) contributes to safeness and success of surgery operations by means of displaying anatomical structures and showing correlative information to surgeons in the process of operation. Based on analysis of requirements for ICAS, a new concept of clinical knowledge-based ICAS was proposed. Designing a reasonable data structure model is essential for realizing this new concept. The traditional data structure is limited in expressing and reusing the clinical knowledge such as locating an anatomical object, topological relations of anatomical objects and correlative clinical attributes. A data structure model called mixed adjacency lists by octree-path-chain (MALOC) was outlined, which can combine patient's images with clinical knowledge, as well as efficiently locate the instrument and search the objects' information. The efficiency of data structures was analyzed and experimental results were given in comparison to other traditional data structures. The result of the nasal surgery experiment proves that MALOC is a proper model for clinical knowledge-based ICAS that has advantages in not only locating the operative instrument precisely but also proving surgeons with real-time operation-correlative information. It is shown that the clinical knowledge-based ICAS with MALOC model has advantages in terms of safety and success of surgical operations, and help in accurately locating the operative instrument and providing operation-correlative knowledge and information to surgeons in the process of operations.     

Power network parameter estimation method based on data mining technology

The parameter values which actually change with the circumstances, weather and load level etc.produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was impliedto treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.     

计算机数据安全隐患及防护措施

从分析计算机数据的物理安全和逻辑安全两个方面入手,对电磁波辐射、联网及病毒对计算机数据安全的威胁进行了论述,指出这是威胁计算机数据安全的主要因素,并提出了数据备份、数据安全删除、辐射屏蔽、病毒及入侵防护、文档加密等相应的安全防护措施和方法。     

中美城市交通模型现况评估

交通需求模型预测未来一个区域内对交通系统的需求.这些模型是辅助交通官员与专家对重大交通项目投资和政策策略作出理性决定的重要工具.根据本刊2008年第1期对中国具有代表性的几个城市交通模型系统的示例介绍,对中国目前的交通模型研发、数据及应用进行总结,并比较了中美交通模型的差异.通过对现阶段中国交通模型建设和使用的深入了解,并结合已有研究和实际工作经验,对中关交通模型及其相关的工作进行系统性和前瞻性的讨论.     

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随着中国城市群的快速发展,城际交通出行环境发生了巨大的变化,因此城际出行者也会不断地重建自己的出行习惯,这就要求建立动态模型研究城际出行者出行行为和预测城际交通需求。本文调查出行者在宁杭城际高铁开通前后两个时期的出行信息,并且引入状态依赖变量表征出行者之前选择结果对之后出行选择的影响,建立了基于面板数据的城际出行方式选择动态模型。模型结果表明,基于面板数据的动态模型比传统的基于出行者单次出行数据的模型拥有更高精度。同时本文根据宁杭城际出行背景设置三组政策变化方案预测出行分担率,结果表明,当选择环境发生变化时,传统模型会高估出行方式分担率的变化程度。以上结论能更好地服务于中国城际交通的规划。     

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大数据所涉及的信息量规模巨大,大数据技术使得人类认识世界的思想及方法发生变革。智慧物流是利用集成化、智能化、移动化技术,使物流系统具有思维、感知、学习、推理判断和自行解决物流中的某些问题的能力,它包含了智能运输、自动仓储、动态配送及智能信息的获取、加工和处理等多项基本活动,为供方提供最大化的利润,为需方提供最佳的服务,同时也应实现消耗最少的自然资源和社会资源,从而形成完备的智慧物流综合管控体系。论坛的主题是大数据与智慧物流,面向大数据技术在智慧物流领域的应用问题,着重研讨大数据背景下智慧物流体系、理论和方法,推动我国现代物流行业健康有序协调发展。     

城市交通状态的空间依赖性和异质性分析

有关城市交通状态空间特征的研究相对匮乏。从空间角度分析城市宏观交通特征,首先,针对城市路网几何形态和交通数据的关系提出适合于城市交通数据分析的空间模型。然后,以从长期的浮动车数据中提取的路段行程速度作为描述交通状态的实验数据,分别采用空间自相关和核密度估计法定量分析路网行程速度的空间依赖性和空间异质性,并分析其在典型时段的空间分布特征。结果显示:城市交通状态的关联性在空间上随着路段间距的增长而衰减;城市交通状态的空间分布特征随时间变化,受路网结构和区域功能的影响在空间中呈现集聚特征。     

Geoswath Plus多波束水深数据抽稀算法研究

根据多波束数据量大的特点,采用VC++程序开发语言,结合工程实际需要,开发出一套多波束数据抽稀程序,有效解决了用户从原始多波束水深数据中提取所需数据的问题。