高速公路空心板桥加宽改造刚度渐变技术

以京港澳高速公路空心板桥加宽改造工程为背景,采用有限元程序MIDAS/Civil建立某20 m跨径加宽空心板桥的空间有限元模型,对布设横向刚度渐变构件前后的加宽空心板桥静力性能进行分析。研究结果表明:布设横向刚度渐变构件前后的空心板桥在新旧桥板过渡区域的位移曲线有显著区别,布设横向刚度渐变构件前,新板和旧板之间出现较大的刚度突变,引起位移曲线突变;布设横向刚度渐变构件后,各块桥面板的位移曲线呈现逐步平缓变化的趋势;布设横向刚度渐变构件前加宽空心板桥的荷载横向分布系数峰值明显,表现出显著的单板受力现象;布设横向刚度渐变构件后,加宽空心板桥的荷载横向分布系数峰值减小,表明所承受的集中荷载由多块桥板共同承担;对比布设横向刚度渐变构件前后加宽空心板桥的静力性能,证明了布设横向刚度渐变构件的有效性,横向刚度渐变效果良好。     

大跨度混凝土连续箱梁桥日照温度场试验及仿真分析

为研究大跨连续箱梁桥的日照温度场分布特点与最不利温度梯度模式,以唐山曹妃甸工业区纳潮河2#大桥施工阶段实桥监测为基础,基于太阳物理学、传热学等相关理论,建立箱梁温度场的热边界条件。参考相关文献确定有限元瞬态热分析的环境参数与热工参数,运用ANSYS软件模拟日照下箱梁的瞬态温度场分布并与实测值进行对比分析,采用最小二乘法拟合出箱梁竖向最不利正温度梯度。研究表明:箱梁截面二维温度场近似关于桥轴线对称分布;顶板、底板、东腹板、西腹板内外表面日照最大正温差出现时刻分别为14:00、15:00、11:00、17:00;混凝土导热性能差,内表面峰值温度出现时刻滞后外表面2~3 h;唐山市曹妃甸区箱梁截面的竖向温度梯度为Ty=19.2e-4y;环境与热工参数选取合理,采用有限元软件ANSYS进行数值模拟具有较高精度。     

日喀则至亚东铁路线路限制坡度方案研究

日喀则至亚东铁路需穿越喜马拉雅山脉,需克服南北麓的巨大高差,其中最困难段帕里至亚东段航空距离37 km,高差1 300 m。考虑本线研究年度内客货运量相对较小,本段应采用较大的线路坡度,选择合理的机车类型,对缩短线路长度,降低工程投资有重要意义。通过从地形地貌、机车性能、工程设置、运营安全及投资等方面对不同坡度方案进行分析比选,推荐日喀则至亚东铁路帕里至亚东段采用30‰限制坡度方案及HXD2双机牵引,研究成果对高差大的地形条件下限制坡度的选择具有一定的指导和借鉴意义。     

《铁路线路设计规范》主要修订内容解析

《铁路线路设计规范》是铁路设计专业性技术标准之一。原标准施行期间也是我国铁路快速发展的十年,随着高速铁路大规模建设,碰到了很多新情况新问题,也积累了很多的工程建设和运营经验,特别是近年来高速铁路、城际铁路、重载铁路在线路设计、施工和运营方面的实践经验,因此本次修订在吸纳近年来铁路科技进步、技术装备发展的最新成果,充分总结以往经验的基础上,全面整合高速铁路、城际铁路、客货共线Ⅰ级和Ⅱ级铁路、重载铁路的线路设计标准,新标准全面涵盖了铁路线路设计的相关内容。就本次修订的依据、原则、修订的主要内容和重点条款进行阐述,供从事铁路设计、施工、建设的技术人员更好地理解和应用规范参考。     

轨道交通U型梁桥下部结构纵向水平线刚度合理值研究

针对轨道交通U型梁桥的具体结构形式,建立了桥上无缝线路梁轨相互作用空间非线性有限元计算模型。分析了轨道交通U型梁桥的无缝线路纵向附加力,并对轨道交通U型梁桥下部结构纵向水平线刚度合理值进行探讨。研究结果表明,我国《地铁设计规范》及《京沪高速铁路设计暂行规定》等规定的桥墩墩顶最小纵向水平线刚度值应用于轨道交通高架桥设计明显偏大,可适当放宽。     

考虑道路因素的智能车辆纵向运动决策与控制研究

针对智能车辆纵向运动时的交通道路适应性问题,考虑路面附着系数和前车运动速度等因素,研究了智能车辆纵向运动决策与控制方法。论文研究了基于车头时距的纵向运动决策方法并建立不同驾驶行为的目标车速模型,运用变论域模糊推理算法设计了目标加速度模型。基于纵向动力学模型,运用自适应反演滑模控制算法建立了驱动控制器和制动控制器。对高附着系数路面和低附着系数路面的行驶工况进行仿真试验验证,结果表明,在不同的附着系数路面和前车变速行驶条件下,智能车辆能实时、合理地决策目标车速、目标加速度,实现安全、高效、稳定的跟驰。     

兰州地铁地温分布特性及其演化规律

以兰州地铁所在地区为研究对象,实测地铁隧道开挖前的地温(简称为初始地温),根据实测数据,提出地铁初始地温预测模型公式。采用非稳态传热的数值模型,分析运营条件下地铁隧道围岩温度的演化规律。结果表明:兰州地铁初始地温随环境气温和埋深的变化而变化;年变温层位于自地表至埋深12m处;年恒温层位于埋深12m及其以下,温度为15℃左右;年变温层中,1年内初始地温变化规律与环境气温变化规律相似,近似呈正弦曲线状分布,但存在相位滞后的现象;1年中初始地温的振幅随埋深的增大呈指数下降趋势。在隧道内空气与围岩之间热交换中,兰州地铁隧道围岩的温度及其梯度、热透厚度(未达到极限时)均与隧道内环境温度、热交换时间成正相关关系,但与距隧道内壁的距离成负相关关系。     

Privacy-preserving MaaS fleet management

On-demand traffic fleet optimization requires operating Mobility as a Service (MaaS) companies such as Uber, Lyft to locally match the offer of available vehicles with their expected number of requests referred to as demand (as well as to take into account other constraints such as driver’s schedules and preferences). In the present article, we show that this problem can be encoded into a Constrained Integer Quadratic Program (CIQP) with block independent constraints that can then be relaxed in the form of a convex optimization program. We leverage this particular structure to yield a scalable distributed optimization algorithm corresponding to computing a gradient ascent in a dual space. This new framework does not require the drivers to share their availabilities with the operating company (as opposed to standard practice in today’s mobility as a service companies). The resulting parallel algorithm can run on a distributed smartphone based platform.     

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.     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.