交通冲突技术在ETC安全评价中的应用研究

由于数据缺乏,国内外有关ETC对交通安全影响的评价研究开展甚少。ETC系统对交通安全究竟会产生怎样的影响,如何进行分析评价,是一个全新的课题。作者参考了其它交通设施的安全评价方法并结合我国交通安全管理的实际情况,得出了仅运用事故统计数据模型来评价ETC对收费区安全水平的影响是欠可靠的这一结论,确定采用以非事故间接评价方法暨交通冲突技术对ETC安全进行评价。本文旨在确立ETC系统的安全评价方法并搭建其评价框架,着重探讨了如何在收费区引入交通冲突技术。     

驾驶员安全可靠性多因素分析

影响驾驶员安全可靠性的身心因素很多,研究中对22项指标进行分析,将有关指标合并,归纳为6种主因子:第一主因子相关变量5种,反映驾驶员的反应能力;第二主因子相关变量4种,反映中枢神经系统的平衡控制能力;第三主因子相关变量5种,反映驾驶员视觉功能;第四主因子相关变量4种,反映注意综合品质;第五主因子相关变量3种,反映驾驶员速度判断能力;第六主因子为驾驶员听觉能力。基于主因子分析进一步整合,建立驾驶员安全可靠性评价指标体系。     

回复反射器的理论

回复反射器以其独特的过反射性能广泛应用于交通器材领域，在保证行车安全方面发挥着重要作用。本文详细论述了回复反射器的反光原理、制造技术及反光性能检测技术，指出了回复反射器在汽车工程的应用中应注意的事项。     

关于一、三级公路安全性认知因素的试验建模研究

在实测86处一级公路典型路段和188处三级公路典型路段道路结构基础上,确定驾驶员对国道一、三级公路的安全性认知因素集,进而针对一级公路的38处样本路段和三级公路的77处样本路段,组织47名驾驶人员进行现场认知评价试验,并应用模糊集合原理和模糊统计方法对评价试验得到的2829组(一级路673组、三级路2156组)有效认知试验评语数据进行分析处理,得到一、三级公路安全性认知因素的模糊评价隶属函数,从而给出驾驶人员对一、三级公路道路条件和交通环境的安全性模糊评价模型。     

重庆马桑溪大桥施工过程稳定性分析

主要研究重庆马桑溪大桥在施工过程中的稳定性。考虑混凝土斜拉桥结构的非线性和构件的极限承载能力,计入施工过程的变形和应力的叠加效应,完成该桥线弹性稳定性和非线性稳定性分析,并对桥梁的非线性稳定性的评判进行了讨论。     

赣江大桥公路桥病害检测与安全评估

首先对赣江大桥公路桥进行病害调查,评定全桥各部位损伤状态。根据评定结果,再对材料退化、结构损伤与受力性能进行实桥测试。应用断裂力学方法,采用观测和超声波探测方法确定初始裂纹尺寸,通过裂纹扩展模拟得出临界杆件的剩余寿命。综合实测数据与理论分析,评估该桥使用安全性和剩余寿命,并建议维护加固措施。     

江苏省交通事故时间分布分析

根据江苏省有关交通事故数据,利用统计方法对交通事故发生的时间特征进行分析。通过分析交通事故年时间分布规律的波峰曲线,得出江苏省交通事故发生数量开始振荡变化,逐年上升的势头有望于近期遏制。通过分析交通事故、月、周、小时时间分布曲线,得出交通事故高峰小时出现在交通量高峰小时之后的9~12h、14~16h,两者不重合。死亡高峰时段(危险时段)多发生在18~21h的3h内。研究结论对不同时间内如何采取不同的安全对策来降低江苏省交通事故具有指导作用。     

基于Prover的联锁软件形式化验证过程研究

计算机联锁系统是铁路信号控制系统的核心设备,是有着苛刻安全要求的复杂控制系统。本文采用Prover形式化开发工具对联锁软件安全需求进行形式化验证。通过模型检验的形式化验证方法,遍历系统输入变量的所有状态空间.验证联锁特定应用满足系统安全需求,确保系统的安全性得以正确实现。在保证系统安全性的基础上,以全状态空间查找反例的检验方法进一步提升产品的质量。     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

Theory of safety surrogates using vehicle trajectories in macroscopic and microscopic settings: Application to dynamic message signs controlled traffic at work zones

This paper presents a new mathematical framework for obtaining quantitative safety measure using macroscopic as well as microscopic traffic data. The safety surrogate obtained from the macroscopic data is in terms of analysis performed on vehicle trajectories obtained from the macroscopic data. This method of obtaining safety measure can be used for many different types of applications. The safety surrogate for the traffic dynamics are developed in terms of a new concept of Negative Speed Differentials (NSD) that involve a convolution of vehicle speed function obtained from vehicle trajectories and then performing the integration of the square of the output for its negative values. The framework is applicable to microscopic traffic dynamics as well where we can use car following models for microscopic dynamics or the LWR model for macroscopic dynamics. This paper then presents the use of this new safety surrogate on the development of a feedback control law for controlling traffic in work zones using Dynamic Message Signs. A hybrid dynamics model is used to represent the switching dynamics due to changing DMS messages. A feedback control design for choosing those messages is presented as well as a simple simulation example to show its application.