面向智能车辆的现役道路设施行驶适应性研究综述

为了总结面向智能车辆的现役道路设施行驶适应性，即现役道路基础设施承载智能车辆行驶的适宜程度，阐述自主智能驾驶定义与驾驶自动化等级分类，在此基础上剖析不同等级间的人机功能差异，并分别从感知层、感知-决策层、决策-控制层探讨与道路设计要素相关联的人机功能差异，通过归纳总结智能车辆与道路几何要素、路面性能及其他道路要素(如道路标线)的相互作用机制研究，从道路工程角度及其他道路要素方面回顾该领域的研究现状，指出存在的问题和未来发展方向。研究结果表明：相比传统车辆，配置高等级自动驾驶系统的智能车辆对现役道路设施行驶适应性最高，主动安全系统次之，而驾驶辅助及有条件自动驾驶系统适应性不足。而目前研究主要问题包括：难以归纳、标定不同驾驶自动化等级间的人机功能差异及其对于道路设计参数的需求设计值；测试道路场景条件过于理想，考虑的驾驶自动化等级单一，试验规模和样本有限；道路几何、路面性能以及道路标志、标线等道路要素与智能车辆间的相互作用机制研究不足，缺乏与不同道路场景相匹配的智能车辆驾驶特征数据的获取手段。因此建议：重视并推动与道路设计要素相关联的关键人机功能差异指标信息共享；联合高保真且可交互的道路场景、高精度感知传感器物理模型、车辆动力学模型及微观交通流模型，利用测试场景自动化生成、极限工况场景搜寻与泛化等技术开展智能驾驶虚拟测试，突破现有研究的深度和广度；探索反映不同等级智能车辆的道路行驶适应性特征指标与评价标准，精准、有效地评估预测复杂道路场景及不利道路条件下的行驶适应性。     

Are French millenials less car-oriented? Literature review and empirical findings

Generation effects play a key role in shaping long-term trends in travel behaviors. Though cohorts born until the 1970s have been increasingly car-focused, a reversal of this trend was noticed among the millenials. Determining whether this break-in-trend resulted from changes in living conditions and economic difficulties, or demonstrates a shift in attitudes away from the car, is critical to future travel trends. We bring a contribution to this debate in the French context, through a literature review followed by empirical findings, using the French Base of Local Household Travel Surveys. Through age-cohort analysis, we find evidence of changing travel patterns among the millenials, taking the form of a shift from driving to transit, along with a decline of car ownership. However, travel attitudes of the millenials play little role, as they do not differ substantially from their elders. Besides, we show that generation effects disappear once a large number of structural factors are controlled for. It looks like the main driver of change in travel behaviors comes from a shift in residential patterns, in relation with longer studies and a delayed entrance into the workforce, and possibly because of increasing work pressure, degraded transport conditions and changes in residential attitudes and desired lifestyles. In the end, these assumptions should be further explored, along with complementary research tracks, including the role of economic factors, the effects of learning experience, as well as heterogeneity in travel patterns, in relation with issues of social and spatial equity.     

高速公路计重收费面临的问题与应对措施

阐述了高速公路计重收费面临的问题,提出应对措施。     

Residential segregation and commuting patterns of migrant workers in China

In China, many rural migrant workers experience residential segregation and live in urbanizing villages, due to China’s unique institutional context (e.g. land tenure system, hukou system) as well as exclusionary housing regulations. The purpose of this paper is to investigate whether these urbanizing villages provide good job accessibility for rural migrant workers. We explore this problem by investigating the commuting patterns of migrant workers. Through a survey conducted in 2009 across four mega-regions in China that are currently experiencing rapid urbanization, we collected individual level information on rural migrant workers’ commute distances and durations, demographics, household socioeconomic status, and whether the migrant worker is living in an urbanizing village or not. From residential address information collected in the survey, we constructed a group of built environment variables. Using IV Tobit models to address the endogeneity issues associated with residential location choice, our analyses show that these urbanizing villages actually provide relatively good accessibility to job opportunities. This result is different from what is suggested by the spatial mismatch literature based on U.S. data. This research helps to fill the gap in the literature on the relationship between residential segregation, built environment and travel behavior in the Chinese context. The findings have implications for policymaking, especially when many government officials are proposing to demolish urbanizing villages without fully realizing the benefits of these villages. This research could also provide useful information for other developing countries facing residential segregation of migrants or immigrants.     

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.     

磁力摩擦式辅助驱动在TBM连续皮带机中的应用

长大隧道TBM(tunnel boring machine)施工大多采用连续皮带机出渣,当运输距离较大时通常需要在中间增设辅助驱动,以降低胶带最大张力,从而降低胶带强度。为了提高辅助驱动的工程适应性,研发了磁力摩擦式辅助驱动系统,具有驱动力大、无二次转载、胶带无二次冲击损伤、结构简单、故障点少、结构紧凑占用空间小、安装方便和配置灵活等特点。该成果可用于采用钢丝绳芯胶带的TBM连续皮带机、支洞皮带机以及其他需要辅助驱动的胶带输送机,并在山西省中部引黄工程TBM1标段成功应用,具有广阔的推广应用价值。     

高压共轨高速电磁阀自升压双电源驱动方法研究

在对现有高压共轨高速电磁阀驱动电路进行分析的基础上,提出了一种基于电磁阀自身的自升压方式,一方面把高速电磁阀关闭时本身储存的电能回收到储能电容中,另一方面在两缸工作间隙利用电磁阀线圈作为升压电路电感对储能电容进行能量补充,保证储能电容上的电压达到一个稳定状态。电路在保证实现双电压快速驱动的同时,使驱动结束的能量得到有效回收,同时省去外接专用升压电路所需要的电感部件,减小了电源电路的体积和设计成本,并且电控单元印制线路板的电磁兼容性设计易于保证。应用该方法对某高压共轨部件高速电磁阀进行试验,匹配出驱动参数,并进行稳定性试验验证,结果表明该方法能够满足工程应用需求。     

加法与减法——中美城市交通综合治理的观察和思考

基于对中美城市交通的观察和思考,阐述不同的城市用地模式决定了不同的大众出行方式选择。借鉴美国的经验与教训,结合中国城市现状和发展需要,寻求缓解城市交通拥堵的方法与措施。指出中国大城市,特别是特大和超大城市,应重视公共交通系统的结构调整和完善。一是继续大力发展具有较高机动性和大运量的高铁、地铁和轻轨系统,构筑城际直达以及沿主要交通走廊布线的轨道交通网络层次;二是在此基础上,梳理地面巴士系统,使之定位于满足可达性需求的支线集散网络层次。最后,还探讨了其他一些相关措施,以减少低速、低效出行,做好综合治理城市交通的"加法"与"减法"。     

不同土质下预制方桩沉桩承载力特性分析

对沿海地区某码头预制方桩沉桩过程进行监控。结果表明,打桩时由于桩周土扰动剧烈,桩的贯入阻力大幅减小,粉质土贯入阻力降低比例高于粉砂。复打测试表明:粉质土的恢复系数较粉砂高;桩侧摩阻力与土层性质密切相关。在沉桩时可根据土层情况选用合适的打桩锤击力,以减小或避免对桩的不利影响。