Bayesian network for red-light-running prediction at signalized intersections

Abstract

Red-light-running (RLR) is an important reason for the large number of intersection-related fatalities, injuries, and other losses. The accurate RLR prediction can effectively reduce crashes caused by RLR behavior. The RLR prediction is usually composed of two parts: the vehicle’s stop-or-go behavior and the arrival time when the vehicle reaches the stop line. Previous stop-or-go prediction models are usually based on embedded traffic sensors using machine learning algorithms. While based on the continuous trajectories collected by radar sensors, RLR prediction can be conducted more effectively. In this paper, a probabilistic stop-or-go prediction model based on the Bayesian network (BN) is proposed for RLR prediction. We extend the deterministic output into the probabilistic output, which provides decision-makers with greater autonomy. The causality of BN improves the interpretability of the prediction model. The BN model is calibrated and tested by the continuous trajectories data measured by radar sensors installed at a signalized intersection. We not only consider the movement measurements of individual vehicles (e.g., speed and acceleration), but also take into account the car-following behavior. As a comparison, different machine learning models and the model based on the inductive loop detection (ILD) are adopted. The results show that the proposed BN model has a high prediction accuracy and performs better in the feature interpretation. This paper provides a new way for probabilistic RLR prediction based on continuous trajectories, which will significantly improve traffic safety of signalized intersections.     

基于桥梁挠度长期监测值的动态预测

文章针对重庆高家花园嘉陵江大桥实时健康监测系统的挠度长期监测数据,根据监测信息的时间序列呈季节、循环等非平稳状态特点,介绍采用ARMA时间序列预测模型,对挠度监测数据中所包含的外荷载的变化趋势及结构抗力的衰变信息进行动态预测,同时建立了结构外效应的预测函数。结果表明,采用低阶模型能对挠度监测值进行较好的动态预测。     

汶川地震震中区高速公路隧道超前地质预报方法

映汶高速公路公路位于四川汶川大地震震中,受地震强烈构造作用影响,隧址区岩体裂隙发育,松散破碎,存在构造富水,地震垮塌堆积体众多,地质条件极为复杂。文章根据本工程隧道的工程地质问题,通过对国内外隧道超前地质预报常用方法的分析,提出了针对本工程的隧道超前地质预报方案,并对地质预报的实施应用及效果进行了举例说明。     

高埋深软硬岩互层地质条件下敞开式TBM岩爆段施工方法研究

基于喜马拉雅山区域某软硬岩性互层地质条件下的深埋隧洞工程,为解决该区域TBM施工过程中的岩爆问题,统计分析了TBM施工过程中的岩爆特征;在施工过程中开展了多种岩爆规避试验,形成了一套较完整的有针对性的施工方法： 1）岩爆风险巡查常态化; 2）超前地质预报和超前处理措施相结合; 3）支护材料和支护方式合理化; 4）掘进参数动态调整。在后续的施工中加以应用,取得了较好的效果,可以为类似地质条件下的安全施工提供参考。     

适于TBM施工的HSP法实时预报技术设计与实现

为实现TBM施工隧道的实时地质预报,以TBM刀盘滚刀破岩震动作为震源的HSP法地质预报技术为基础,通过分析TBM施工工艺与机身结构特点,对HSP系统硬件和软件进行优化设计,使其小型化、自动化和智能化,并搭载于TBM上进行智能控制。在TBM掘进过程中,首先,通过隧道轮廓位置的检波器连续或高频次地采集地震反射波信号; 然后,经系统软件对数据的自动处理,实时获取掌子面前方地层反射特征参数图谱; 其次,通过智能识别技术完成对不良地质反射界面的有效拾取与判识; 最后,实现不良地质的探测。通过对适于TBM施工的HSP法实时预报技术进行优化设计,实时探查前方地层不良地质(体)位置、规模与性质,为TBM的高效施工起到一定的推助作用。     

Predictive power of inspection outcomes for future shipping accidents – an empirical appraisal with special attention for human factor aspects

This paper investigates whether deficiencies detected during port state control (PSC) inspections have predictive power for future accident risk, in addition to other vessel-specific risk factors like ship type, age, size, flag, and owner. The empirical analysis links accidents to past inspection outcomes and is based on data from all around the globe of PSC regimes using harmonized deficiency codes. These codes are aggregated into eight groups related to human factor aspects like crew qualifications, working and living conditions, and fatigue and safety management. This information is integrated by principal components into a single overall deficiency index, which is related to future accident risk by means of logit models. The factor by which accident risk increases for vessels with above average compared to below average deficiency scores is about 6 for total loss, 2 for very serious, 1.5 for serious, and 1.3 for less-serious accidents. Relations between deficiency scores and accident risk are presented in graphical format. The results may be of interest to PSC authorities for targeting inspection areas, to maritime administrations for improving asset allocation based on prediction scenarios connected with vessel traffic data, and to maritime insurers for refining their premium strategies.     

基于三点模型的柴油机热工参数预测

准确预测热工参数劣化趋势是对柴油机实施预测维修的前提。已有预测技术均视不同时段信息所具有的建模价值相等,忽略了近期信息比远期信息更能反映设备当前运行态势这一客观事实,导致预测模型难以准确描述柴油机热工参数的真实变化规律。针对该问题,提出一种基于三点模型的柴油机热工参数预测方法,并采用新陈代谢法同步更新建模数据,以提高建模数据的利用效率及预测精度。与灰色系统GM(1,1)模型预测精度的对比结果表明,三点模型能够有效处理不同时段信息在反映设备运行态势能力上的差异,得到更为准确的预测结果。     

基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

基于LSTM的舰船运动姿态短期预测

舰船的六自由度运动状态形成复杂的非线性过程,运动姿态会受到耦合作用、不定周期、噪声信号以及混沌特性等因素的干扰,因此很难得到精确的预测结果.为了提升舰船运动姿态的预测精度,利用舰船时间序列的特点,建立了基于长短期记忆单元(LSTM)模型,对其进行了舰船姿态预测仿真,将结果与时间序列分析法的结果进行对比.实例分析表明:基于LSTM模型的预测方法具有精确度高、易实现的特点.这为舰船运动短期预测提供了一个新的思路和方法.