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.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

也论柴油轿车在国内市场的发展前景

研究了世界范围内柴油机在乘用车领域内的应用现状,阐述了柴油机作为车用动力的优势。根据国内的政策环境与市场环境,分析了排放法规、柴油品质对市场的影响。根据对柴油轿车的性能要求,包括振动和噪声、排放等,分析了更适合我国轿车柴油机发展的技术路线,并重点对若干关键技术和难点进行了剖析。柴油机技术进步和市场化应用表明,柴油轿车在我国具有广阔的应用前景,政府和企业应做好政策和技术上的准备。     

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研究了高峰时段列车运行的衔接协调对换乘站候车客流量的优化问题。首先分析了换乘站各站台候车客流的组成因素,确定出各运行方向的换乘站台内客流量随时间变化的规律。然后,以时段内换乘站候车客流量的最大值最小为目标,建立优化模型。模型以站台最大可容纳候车人数为约束条件,以各方向列车在换乘站的到达时刻为调整对象,实现了高峰时段换乘站内聚集客流的优化。最后,针对验证案例,设计了遗传算法进行求解,得出了协调较优解,并给出与较劣解的对比分析。结果表明,该优化方法能够有效降低换乘站内的候车客流人数,可为网络化的优化协调工作提供参考。     

飞机防冰腔结构参数的重要性测度

分析了常见的3种飞机防冰腔结构,应用Gambit软件建立了双蒙皮防冰腔结构网格模型。采用Spalart-Allmaras湍流模型模拟热气在防冰腔内的流动状况,采用Fluent软件进行传热效率分析,建立了防冰腔结构参数对传热效率的重要性测度模型。通过随机响应面法建立防冰腔结构参数与传热效率的函数关系,采用低分散性抽样法求解防冰腔结构参数的重要性测度,建立了防冰腔结构参数的重要性测度分析流程。分析结果表明:当笛形管中心到外蒙皮的距离从35.15mm增加到38.85mm时,传热系数由0.505减小到0.463;当双蒙皮通道高度从2.85mm增加到3.15mm时,传热系数由0.495减小到0.476;当射流孔孔径从1.90mm增加到2.10mm时,传热系数从0.505减小到0.494;当射流孔角度从14.25°增加到15.75°时,传热系数从0.476增加到0.494。防冰腔结构参数的重要性排序依次为射流孔角度、笛形管中心到外蒙皮距离、射流孔孔径、双蒙皮通道高度,在防冰腔结构加工与装配过程中,需要重点考虑射流孔角度与笛形管中心到外蒙皮距离这2个参数。     

Er~(3+)及Er~(3+)/Yb~(3+)掺杂YBa_3B_9O_(18)材料的发光性能

通过高温固相烧结法制备了Er3+掺杂以及Er3+/Yb3+共掺杂YBa3B9O18荧光粉.X射线衍射结果表明,Er3+/Yb3+的掺杂并没有改变基质的六方结构.应用325 nm激光激发Er3+掺杂YBa3B9O18,观察到了Er3+离子的特征发射峰,证明了Er3+离子与基质间的能量传递.应用980 nm激光分别激发Er3+掺杂以及Er3+/Yb3+共掺杂YBa3B9O18荧光粉,观测到了Er3+离子的上转换过程,该过程可通过能级图进行解释.实验结果表明Yb3+对Er3+的上转换发光有敏化作用.     

不锈钢地铁车辆侧墙焊缝缺陷率分析

由于非熔透型激光叠焊工艺的局限性,侧墙焊缝处会产生内外钢板局部未焊透的现象即焊缝缺陷.为研究这种焊缝在不同缺陷率下的拉剪强度及其对车体静强度的影响,结合某不锈钢地铁车辆激光焊车体,通过建立不同焊缝缺陷率的车体有限元模型,分析各个工况下不同焊缝缺陷率的车体侧墙焊缝剪应力的变化,以此确定在静载工况下不锈钢激光焊车体侧墙焊缝缺陷率的限值.     

中国铁路客运枢纽发展回顾与展望

随着中国新一代高铁枢纽的陆续建成,简单的站前广场换乘模式逐渐让位于全天候、无障碍、人车分行的换乘空间接驳模式。新型枢纽的空间特征、核心价值与局限性亟待总结。基于国内外铁路客运枢纽发展历史与背景的研究,将铁路客运枢纽按照空间特征分为三种类型:传统铁路客运站、铁路交通综合体和客站城市综合体。从客流需求变化、城市发展背景、运营管理差异三方面详细分析国内外铁路客运枢纽空间模式选择的影响因素。结合中国高铁建设机制的变迁,提出未来中国铁路客运枢纽的发展方向:大城市高铁枢纽应强化与城市空间的衔接,加强与城市商业功能的有机结合,由铁路交通综合体向客站城市综合体转型;中小城市高铁枢纽应采取更加灵活、集约、经济的形式,谋求特色产业与枢纽交通功能的结合。     

深圳市交通需求管理政策的法治思考

深圳市制定和实施了若干小汽车交通需求管理政策,完善相关政策的法制保障成为政策实施的关键问题。首先结合深圳市交通需求管理实践经验,梳理小汽车增量调控、停车收费政策的制定与实施情况。指出交通需求管理政策的制定与实施面临经济杠杆存在法理争议、行政手段法律依据不足和程序合法性存疑等法治障碍。进而提出在国家层面构建交通管理法治体系、在地方层面完善法律法规以及规范交通需求管理政策出台程序等完善路径。     

产业转移背景下西部综合运输系统发展对策

本文简要分析了当前西部地区承接东部地区产业的特点,结合西部地区社会、经济实际情况分析了当前西部综合运输系统发展现状,主要为基础设施建设缓慢、总体上发展滞后、能耗高及污染严重,不具有可持续性等;并阐述了产业区域转移背景下西部综合运输系统发展面临的新挑战,包括加剧了西部综合运输系统运能与运量矛盾,加重了西部综合运输系统发展的资源和环境压力,并对西部综合运输系统的协调发展能力提出了更高的要求;最后就西部综合运输系统未来的发展提出建议,即西部综合运输系统的发展要以美丽中国和生态文明理念为指导,考虑产业转移诱发的运量需求增加,并把产业转移引起的资源环境约束纳入西部综合运输系统整体发展战略中,最终实现西部综合运输系统持续、快速发展。