城乡快速干道车-人冲突时间窗预测模型

为了解决城乡快速干道车-人冲突和事故严重的问题，将车-人冲突点的分析方法扩展到车-人冲突时间窗的分析方法，构建一种行人运动轨迹实时监测和穿越时间预测相结合的车-人冲突时间窗组合预测模型。首先，分析行人违规穿越的实测数据，确定不同类别行人对应的穿越时间置信区间以及松弛时间；其次，根据运动学模型的预测结果判断行人的所属类别并初步确定行人的穿越时间，同时通过卡尔曼滤波算法对行人穿越过程进行实时监测；再次，融合运动学模型预测结果和卡尔曼滤波监测结果，确定最终的车-人冲突时间窗；最后，对所提出的组合预测模型进行标定和验证，并通过VISSIM仿真平台进行安全性能测试。模型验证结果表明：在正常情况下，该模型能够保障行人的安全且能兼顾松弛时间重置次数；在行人初始穿越速度过低或穿越前、中期存在持续低速的情况下，该模型可以通过多次松弛时间重置来解决模型的适用性问题。安全性能测试结果表明，在车辆行驶时间均值增加4.7%的情况下，安全车辆数占比增加了37.3%，车辆的后侵占时间(PET)测试值则增加53.8%。因此，与无松弛时间的预测模型相比，所提出的有松弛时间的车-人冲突时间窗预测模型能够在对交通效率影响较小的前提下，较大程度地提高车-人冲突的安全性。     

新型防爆地震仪在瓦斯隧道超前地质预报中的应用

为提高瓦斯隧道地震波反射法超前地质预报现场作业的安全性及可操作性，介绍一种新型防爆地震仪的主机结构、主板设计、本安电源及远程控制等核心技术，同时介绍检波器气囊耦合技术；最后采用36道防爆地震仪和高精度三分量地震检波器气囊耦合装置，在重庆渝北至四川广安高速公路华蓥山隧道进口右线K24+314掌子面处进行超前探测试验，其采集的地震记录质量高，气囊耦合方式现场操作方便快捷。经过与传统的国外TSP仪器及钢管锚固剂耦合方式对比分析，充分说明采用国内的矿用防爆地震仪及其配套研制的检波器气囊耦合装置完全适用于各类瓦斯隧道地震波反射法超前地质预报工作，且能使地震波反射法实现本质安全。     

基于形貌特征的CO2腐蚀速率预测方法

针对CO2腐蚀过程复杂，难以利用实测数据有效预测腐蚀速率问题，文中以腐蚀形貌图像为对象，利用支持向量机（SVM）构建预测模型，实现对CO2腐蚀速率的预测。对N80钢的CO2腐蚀图像进行灰度处理、灰度增强及二值化处理，提取蚀孔数目和孔蚀面积。经计算获得孔蚀密度及孔蚀率，结合工作温度及CO2分压作为腐蚀速率预测的四维特征向量。以SVM构建预测模型，经测试，可准确预测CO2腐蚀速率，并与神经网络预测结果进行对比，验证了该方法的优越性。     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.     

Economies of density and factor substitution in Dublin city bus services — a translog approach

The primary aim of the paper is to determine a short‐run cost function for Dublin City Bus Services (DCS), using a translog approach. The results show that there are significant economies of density in the transit operation. The results also indicate that the structure of DCS is characterised by a homothetic production function and that partial elasticities of substitution are non‐unitary.     

汽车制造企业生产管理存在的问题分析及解决办法

文章讲述了在汽车制造公司中生产管理的重要意义;对当前汽车企业生产现状进行了分析并列举了汽车制造企业生产过程中需要注意的事项,汇总了生产管理存在的主要问题,并提出了相应的改善方案,对其他企业的生产管理具有参考价值。     

北京地铁隧道地表横向沉降槽参数分析

为解决北京地铁隧道施工不同影响区划分和影响范围确定的不准确问题,对北京地区13 条地铁线路、903 份隧道工程的地表横向沉降槽资料进行分析,根据施工方法和地层条件的不同,分别对盾构法和矿山法施工隧道在黏性土地层、砂卵石地层等区域的沉降槽Peck 公式拟合参数进行统计分析,得出地层损失率和宽度参数的分布形态、相关统计值以及与隧道相对埋深的相关性。研究结果表明: 1)地层损失率和宽度参数的数理统计结果可以很好地指导北京及类似地层条件的城市地铁隧道工程影响区划分和影响范围的确定; 2)施工方法和地层条件是影响地铁隧道周围地层变形的重要因素,工程地表变形控制应注重相关研究; 3)建议各地深入开展地铁隧道沉降槽的拟合分析研究,为隧道工程影响区划分和影响范围确定提供科学依据。     

基于混沌递进预测模型与趋势检验的深基坑变形规律研究

为提高基坑变形预测精度及稳定性,首先,利用遗传算法优化BP神经网络的结构参数,再将参数优化后的BP神经网络与灰色模型结合,构建出GA-BP神经网络模型,并利用该模型实现基坑变形序列的初步预测; 其次,基于残差序列的混沌特性,再利用混沌理论进行残差优化,进一步构建考虑混沌特性优化的GA-BP神经网络模型; 最后,将SR检验引入到基坑变形趋势判断中,以检验预测结果的准确性。实例检验表明： 通过遗传算法及混沌理论的递进优化,能逐步提高预测精度,验证文章预测模型的有效性,且预测结果与SR检验结果的一致性较好,说明该预测模型的可信度高。     

大瑞铁路高黎贡山隧道原岩温度预测

随着深埋长隧道需求数量的增加,高地温热害问题越来越突出,严重影响到施工人员的安全及工作效率,需要根据地温的分布特征来对隧道高温地段采取必要的降温措施。结合大瑞铁路高黎贡山隧道的地质、地温实测资料及通过“谷地地理信息系统”获取的地形数据,采用基于地质演化历史计算原岩温度的数学模型,编制相应的计算程序对高黎贡山大瑞铁路隧道进行原岩温度预测,并对隧址区的温度场沿洞线方向进行简单划分,为隧道采用有效的降温措施提供必要的地温场数据。通过计算分析可以得出： 1）沿隧道向瑞丽方向,隧道原岩温度有先增大后减小的趋势,最高点温度为31.73 ℃; 2）隧道开始大约2.5 km以内及18 km至隧道终点段,隧道岩温在28 ℃以下; 3）2.5~18 km段隧道岩温为28~31.73 ℃,其中高于28 ℃区域需要采取实时温度监控措施。