基于超融合基础架构的铁路云数据中心建设方案

在充分研究铁路信息化发展现状、演进困境以及云计算平台技术的基础上,结合中国国家铁路集团有限公司对于信息化的总体规划,提出铁路云数据中心的总体技术架构,详细阐述基于云计算技术的铁路数据中心平台优势.对涉及到的关键技术:超融合技术和网络拓扑技术展开说明.立足铁路云数据中心规划建设角度,重点从IaaS层资源规划、云管平台建设...     

智能网联汽车数据安全管理研究

智能网联汽车大数据已经成为推动自动驾驶技术迭代更新,促进产业生态创新发展的基础性战略资源,随之而来的用户隐私和数据安全问题受到了社会各界的广泛关注。分析了智能网联汽车数据区别于一般大数据的典型特征,针对不同类别的数据进行了权属问题研究,认为除基础属性信息外,其他数据都应在匿名处理后进行分析应用。研究提出了目前数据产业化应用的4种典型场景。在国内外关于汽车数据安全保护相关法律法规的框架下,从国家、行业、企业3个层面分析提出了规范数据采集处理、强化数据挖掘应用的策略建议。     

货车轨迹数据在公路货运系统中应用研究综述

随着轨迹数据可获取性及精度的持续提高,货车轨迹数据被广泛应用于公路货运系统的 规划与管理中,同时,人工智能和大数据分析技术的快速发展也为公路货运系统研究带来新的机 遇与挑战。本文全面梳理并总结了公路货运轨迹数据应用领域的相关研究,从基于轨迹数据的 货运出行信息辨识、货运系统关键特征预测、货运轨迹数据进一步应用3个方面回顾现有文献的 研究目标、主要内容和研究方法。通过文献分析发现：货运出行信息辨识研究聚焦于货运停留 点、车辆和货物、活动出行模式等热点主题,但现有辨识方法多移植于旅客出行研究,需要更多地 考虑货运出行的独特特征。在货运系统关键特征预测方面,研究者主要针对货运行程时间、空间 位置、出行需求等主题展开研究,并证明了基于轨迹数据预测货运特征的可行性,但预测时空范 围较为局限,需要根据具体的货运任务、货车司机特征和货运政策进行深入研究。此外,轨迹数 据也被应用于货运出行路径选择行为、货运停车休息行为、行驶安全、货运排放和能耗分析、货运 政策评估等研究。最后,在总结现有研究不足的基础上,本文认为未来研究应重点将货运轨迹数 据与其他多源数据相结合,从3个关键技术进行突破：一是针对货运实践个体,重点探索高效货车 驾驶员的出行特征和出行模式,并在货运系统中进行推广应用;二是针对交通运输新技术和新形 势,重点开发和优化自动驾驶技术和重大应急事件影响下的货运组织模式与策略;三是针对货运 供需关系及匹配机制,重点研究货运全流程供需状态辨识与预测,并结合深度学习等方法训练和 开发智能供需匹配模型,从而优化货运系统调度,助力社会散乱运力资源整合,提高货运系统的 综合效率。     

数据驱动跟驰模型综述

车辆跟驰模型是被交通科学与交通工程领域广泛认可的微观交通流模型,是交通流理论 的基础。近年来,信息感知与获取、大数据、人工智能等技术快速发展,推动了数据驱动跟驰模型 的快速发展。数据驱动跟驰模型,是以真实的车辆行驶数据为基础,利用数据科学与机器学习等 理论和方法,通过样本数据的训练、学习、迭代、进化,挖掘车辆跟驰行为的内在规律。本文系统 回顾了数据驱动跟驰模型在过去20余年的发展历程以及由神经网络和深度学习带动的两次研究 热潮,归纳了基于传统机器学习理论的跟驰模型、基于深度学习的跟驰模型、模型与数据混合驱 动的跟驰模型3类数据驱动跟驰模型,并分别介绍了其中的典型代表。分析数据源发现,尽管各 种高精度轨迹数据不断涌现,目前研究仍多使用美国于2006年发布的Next Generation Simulation (NGSIM)高精度车辆轨迹数据,模型的可移植性和泛化能力值得思考与研究。提出关于模型输 入、输出的3个问题：如何考虑更多驾驶行为变量,是否有必要考虑更多行为变量,现有输入、输出 是否可替换。在模型测试与验证方面,发现并讨论了目前测试不充分、对比不完整、缺少统一测 试集与测试标准等问题。最后,探讨了数据驱动跟驰模型原创性与成功的关键因素等问题。期 望通过本文的梳理,帮助研究者更好地了解数据驱动跟驰模型的过去与现状,促进相关研究的快 速发展。     

新冠肺炎交通防控政策对长沙市人口流动的影响

为探究新冠肺炎疫情下交通防控政策对长沙市人口流动的影响,本文根据长沙市在新冠 肺炎疫情期间颁布的交通防控政策和疫情实时防控情况划分防控阶段,基于百度迁徙大数据,利 用双重差分模型,识别长沙市不同阶段的交通防控政策以及量化防控效果,分析交通防控政策对 长沙市人口流动的影响。结果显示,长沙市在交通管制阶段,平均人口迁出强度、平均人口迁入 强度及城市内部出行强度分别下降了83.68%、69.24%及59.74%,有效地控制了人口流动,降低了 疫情扩散危险。在交通恢复阶段,长沙市人口流动强度逐渐反弹,城市内部出行强度基本恢复到 2019年同期水平。本文研究结果显示了交通管制对疫情扩散限制的有效性,为常态化疫情防控 下精准防控政策和复工复产政策制定提供参考。     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

Flow-based accessibility measurement: The Place Rank approach

In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.     

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.     

Autonomous vehicle perception: The technology of today and tomorrow

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.