长大货物车适应THDS轴温探测技术的研究

针对不适应THDS轴温探测系统的长大货物车,分析论证了现车轴箱改进方案适应THDS轴温探测的不足,并提出了轴箱优化方案。经计算、试验和装车验证表明,新轴箱可行、有效、安全,满足THDS轴温探测要求,保证了长大货物车运输安全。     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

重载货车摩擦缓冲器阻抗特性的数值模拟

为预测摩擦缓冲器的实际工作状态,从几何特征和作用原理的角度,建立详细的MT-2型缓冲器理论模型。首先,通过对缓冲器内部各摩擦元件的运动学和静力学分析,推导出缓冲器在准静态下的阻抗特性;其次,引入附加摩擦系数量化各摩擦元件之间动静态摩擦过渡时的黏滞补偿,并模拟出缓冲器在动态下的阻抗特性;最后,利用C80型货车冲击试验数据对该缓冲器的理论模型进行验证。验证结果表明:总体上,数值模拟和现场试验下的缓冲器示功曲线基本吻合,说明模型的正确性;局部上数值模拟中缓冲器从加载Ⅰ阶段过渡至加载Ⅱ阶段的突变现象在冲击试验中表现的并不明显,还有待进一步完善。     

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

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.     

基于FDS公路隧道火灾仿真研究

针对公路隧道突发火灾事故问题,采用模拟软件对隧道发生的火灾进行研究。分析火灾条件下公路隧道内被困人群的疏散行为特点,给出隧道内人员疏散的安全依据,按照隧道火灾条件下通风风速不同时火灾烟气蔓延情况,确定火灾可利用疏散时间(ASET),通过分析人员疏散特点计算出所需的疏散时间(RSET),通过对比得出:通风风速至少为3m/s时才能保证人员的安全疏散。     

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。     

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.     

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。