Lights,camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles

This study estimates the effect of red light cameras (henceforth cameras) on collisions under the Los Angeles Automated Photo Enforcement Program that ran from 2006 to 2011. To control for selection bias and unobservables, a data set is constructed such that intersections with cameras are compared to control groups of nearby intersections without cameras, matched on observable characteristics. To capture potential spillover effects of cameras, control groups at various distances from the intersections with cameras are considered. A Poisson panel data model with random coefficients is applied to these data and estimated using Bayesian methods. The program suffered from weaknesses in enforcement. The city’s courts did not uphold citations and this dampened the effect cameras had on drivers. These problems are accounted for in modeling. Controlling for these concerns, results indicate that the cameras decreased red light running related collisions, but increased right-angle and injury collisions, as well as collisions overall.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

融合D-S理论的锅炉人因贝叶斯网络安全性定量分析

针对船用锅炉人因安全性分析中存在的知识不确定性,采用D-S证据理论对多专家信息进行融合并建立考虑人因的贝叶斯网络,得到节点条件概率的区间表示形式.经加权平均后代入贝叶斯网络计算,与面向对象贝叶斯网络和FTA等方法的对比显示,该方法能够更加有效地融合不同专家信息,也更为符合工程实际.     

客运专线无砟轨道施工平面控制网优化设计

研究目的:本文通过对客运专线无砟轨道施工平面控制网设计的研究,提出平面控制网的布网方法、精度设计准则。研究方法:采用仿真实验的方法,通过误差理论分析,反演推算各级控制网精度,研究控制网的布网方法和精度设计准则。研究结果:提出了客运专线无砟轨道平面控制网的布网方法及精度设计准则。研究结论:客运专线无砟轨道施工平面控制网的坐标投影长度变形限定值应不大于10 mm/km;控制网沿线路应不大于5 km布设1对相距1 km左右且相互通视的GPS点,在GPS点之间布设近似直伸附和导线,导线边长400~600 m;任意3个相邻控制基桩点位误差引起的角度中误差应不大于8″。     

C~3Ⅰ系统抗攻击能力评估研究

介绍一种评估C3Ⅰ系统抗攻击能力的方法。首先提出用网络熵的方法进行定量分析,然后用模糊综合评判的方法进行评估,最后结合实例对该方法进行验证,得出评估结论。     

铁路客运专线与公路并行区段柔性防护预留变形空间的研究

为保证高速列车的行车安全,必须建立防止异物入侵的安全防范体系。在铁路客运专线与公路并行区段采用柔性防护时,必须预留一定的变形空间,以保证受到异物冲击后的柔性防护网不侵入铁路建筑限界。通过有限元仿真计算和现场性能试验,准确把握柔性防护网在规定的冲击能量作用下的最大变形数据。仿真和试验结果表明:柔性防护网的最大变形量随着冲击能量的增大而增大,最大变形距离达1.8 m以上。建议设置在铁路客运专线与公路并行区段之间的柔性防护网,其垂直冲击能量设计标准取为:一般区段9.3×104J,加强区段1.7×105J;且柔性防护网预留变形空间的宽度以不小于2.0 m为宜。     

铁路信号综合防雷中接地的具体作法

针对现场实际,对铁路信号综合防雷中地网设置的具体作法进行了探索,阐述了设置地网时应当注意的技术问题。     

基于Petri网的虚拟维修手部动素建模

针对虚拟维修仿真中手部动作仿真难度大、不容易控制的问题,提出基于Petri网手部维修动素模型,将虚拟维修手部动作根据维修操作封装为若干动素单元的组合,降低了仿真计算的难度,从而提高手部动作控制的精确性,以螺栓拆卸的手部动作建模过程为例进行验证。