不良地质条件采用TBM的施工方法

基于TBM（Tunnel boring machine全断面隧道掘进机）隧道工程不良地质开挖条件,探讨了围岩岩性、断层、挤压、洞壁及掌子面开挖不稳定性对于设备选型、尺寸制约性的影响,针对不同案例,提出了面临不同施工条件的建议和措施。     

基于脑电波分析技术的安全驾驶实验研究

为了研究饮酒对驾驶过程中脑电波的影响,在由急弯、紧急停车港、障碍区和直线路段组成的虚拟场景中,利用脑电仪实时采集汽车驾驶员的脑电信号,建立脑电波分析指标体系,进行快速傅立叶变换后,计算δ波、θ波、α波、中间快波、β波以及γ波的平均功率,进而计算各频段波功率占总频段波功率的比例,比较组合指标(α+θ)/β在饮酒前后的差异,并运用成对t检验分析各项指标.结果表明,饮酒对θ、α和γ脑电波有显著影响,t检验结果小于0.01;对其余脑电波没有显著影响,t检验结果大于0.05.在3个显著变化的指标中,仅γ脑电波饮酒前、后的差值随着酒精浓度的下降逐渐减小,酒精浓度小于40.5 mg/100 ml后,差值为1.12%.     

Eco-driving training of professional bus drivers – Does it work?

The drive to reduce fuel consumption and greenhouse gas emissions is one shared by both businesses and governments. Although many businesses in the European Union undertake interventions, such as driver training, there is relatively little research which has tested the efficacy of this approach and that which does exist has methodological limitations. One emerging technology employed to deliver eco-driving training is driver training using a simulator. The present study investigated whether bus drivers trained in eco-driving techniques were able to implement this learning in a simulator and whether this training would also transfer into the workplace. A total of 29 bus drivers attended an all-day eco-driving course and their driving was tested using a simulator both before and after the course. A further 18 bus drivers comprised the control group, and they attended first aid courses as well as completing the same simulator drives (before-after training). The bus drivers who were given the eco-driving training significantly improved fuel economy figures in the simulator, while there was no change in fuel economy for the control group. Actual fuel economy figures were also provided by the bus companies immediately before the training, immediately after the training and six months after the training. As expected there were no significant changes in fuel economy for the control group. However, fuel economy for the treatment group improved significantly immediately after the eco-driving training (11.6%) and this improvement was even larger six months after the training (16.9%). This study shows that simulator-based training in eco-driving techniques has the potential to significantly reduce fuel consumption and greenhouse gas emissions in the road transport sector.     

U形-箱形组合结构连续梁力学特性研究

U形-箱形组合连续梁计算现阶段缺乏具体的经验和理论公式,文中在杆系模型分析的基础上,采用通用有限元程序ABAQUS分析了结构典型部位的受力情况,研究了该类桥梁在多种工况下的力学行为和受力机理。结果表明:U形-箱形组合连续梁力学特性明显区别于传统梁型,空间受力特性明显;由于横向变形导致U形梁腹板内、外侧应力存在差异,平截面假定已不适用。     

面向高速公路车辆切入场景的自动驾驶测试用例生成方法

为在自动驾驶汽车基于场景的测试中生成涵盖相应场景中复杂多变的真实交通运行过程的测试用例,从highD数据集中提取车辆切入场景的多个实际样本,通过分析运动参数和参与车辆之间的位置关系,建立车辆切入场景的描述模型,根据切入点的碰撞时间评估该方案的风险程度,并结合描述模型中参数的分布,采用蒙特卡罗方法生成测试用例。结果表明,生成的车辆切入测试用例能够覆盖所有风险等级,可较好地支持自动驾驶测试。     

Estimating the ride quality characteristics of vehicles with random decrement analysis of on-the-road vibration response data

This paper describes the application of a practical analytical technique based on the random decrement method to estimate the rigid sprung mass dynamic characteristics (frequency response function) of road vehicles using only vibration response data during constant-speed operation. A brief history and development of the random decrement technique is presented, along with a summary of work undertaken on optimal parameter selection to establish the random decrement signature. Two approaches to estimate the dynamic characteristics from the random decrement signature are described and evaluated. A custom, single-wheeled vehicle (physical quarter car) was commissioned to undertake a series of on-the-road experiments at various nominally constant operating speeds. The vehicle, also instrumented as an inertial profilometer, simultaneously measured the longitudinal pavement profile to establish the vehicle's actual dynamic characteristics during operation. The main outcome of the paper is that the random decrement technique can be used to provide accurate estimates of the sprung mass mode of the vehicle's dynamic characteristics for both linear and nonlinear suspension systems of an idealised vehicle.     

Big data driven dynamic driving cycle development for busses in urban public transportation

Field-relevant reference driving cycles, equivalent to real-life operation, are a prerequisite for the consistent development and testing of vehicles, their components, and control algorithms. Furthermore they are the basis for certification and type testing. However, a static cycle can easily be detected during vehicle testing, so that optimized control parameters could be used to obtain improved emission results under test conditions. In this paper, a novel method is described and applied to generate a dynamic driving cycle that statistically matches the real-life operation of a vehicle. The analysis is performed based on an extensive field data set obtained during an automated measurement campaign of public busses for more than a full year with 27,365 h of operation and 315,583 km driven in the city of Hamburg (Germany). The data collected is statistically compared to the static reference cycles New European Driving Cycle (NEDC) and Worldwide harmonized Light Vehicles Test Procedure (WLTP). Two micro trip models with increasing complexity are described and fit to the data set. All models are quantitatively compared to the measured data set applying a Quality of Fit (QoF) indicator. Based on the highest consistency to field data, a non-deterministic driving cycle generator is developed and its output is statistically compared to the original measurement. In contrast to the existing reference cycles, the dynamic output of the non-deterministic driving cycle generator presented in this paper is statistically proven to be consistent with real-life operation of public busses in the urban environment of Hamburg.     

基于坐垫体压分布的驾驶员生理状态识别方法

提出一种基于坐垫体压分布识别长途驾驶过程中驾驶员生理状态的方法.开展长途驾驶模拟试验,采集9名被试者在驾驶过程中的坐垫体压分布数据,提取82维分类特征,再采用序列浮动前向选择算法从中选出55维最优特征子空间.试验结果表明,该方法能准确识别驾驶员生理状态,贝叶斯分类正确率可达93.37％.     

手机数据在交通调查和交通规划中的应用

手机作为一种理想的交通探测器,为居民出行信息分析提供了很好的技术选择。将手机数据映射至交通分析单元,并经信息预处理、匹配分析、交通模型分析处理、数据去噪、扩样等一系列海量数据运算处理,最终可获得居民出行特征数据。利用长期历史手机话单数据,可分析常住人口和就业人口分布、通勤出行特征、大区间OD、特定区域出行特征、流动人口出行特征等。手机信令数据能够较完整地识别手机用户的出行轨迹,可进一步应用于分析城市人口时空动态分布、特定区域客流集散、查核线断面或关键通道客流、轨道交通客流特征、出行时耗、出行距离、出行强度、道路交通状态等。根据天津手机话单数据应用案例及上海手机信令数据应用案例,验证了技术可行性。     

Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.