共查询到19条相似文献,搜索用时 78 毫秒
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我国首例闯黄灯行政诉讼案的判决显示出我国交通管理部门在黄灯设置上缺乏统一规范并存在执法误区.通过问卷调查对我国驾驶员对黄灯的认识与建议进行调查,并对黄灯信号产生的历史背景、含义与作用、时长计算以及对交叉口交通安全的影响进行阐述,明确了黄灯与全红信号各自应承担的功能与时长计算.建议现行交通法规对黄灯的规定改为:“黄灯启亮表示交通信号灯即将变为红灯,碰到黄灯时应该减速停车,除非不能安全停车时可以在黄灯期间通过停车线进入交叉口.”同时建议增设闯黄灯超速自动抓拍功能,预防抢黄灯现象的发生. 相似文献
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有无倒计时条件下黄灯第二类困境区域分布 总被引:1,自引:0,他引:1
为进一步论证倒计时对黄灯第二类困境区域分布的影响,以有无倒计时信号交叉口为研究对象,利用视频观测法,采集黄灯启亮时首停车和末行车至停车线的距离和速度,分析有无倒计时条件下驾驶员行驶行为决策,应用二元 Logit 回归分析方法,构建有无倒计时条件下停车概率模型,确定第二类困境区域上下边界,并探讨有无倒计时条件下第二类困境区域分布.结果显示,黄灯启亮时车辆至停车线距离和倒计时增大停车概率,但黄灯启亮时车辆速度减小停车概率;倒计时条件下第二类困境区域移向停车线,但其长度仍为24.1 m.因此,倒计时不能缩小黄灯第二类困境区域.然而,确定第二类困境区域边界过程中未考虑倒计时与其他变量之间的相互作用,这可能导致低估倒计时的效果,后续研究将予以改进. 相似文献
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在信号灯设置中,黄灯起到过渡的作用.理解黄灯期间的驾驶行为对于黄灯期间的安全保护,以及黄灯时长的设置具有重要意义.对比了国内外相关研究的数据获取方法、分析方法,对我国城市道路中2种常见的信号控制的十字交叉口与路段人行横道进行实地调查,利用视频拍摄的方法获取实验数据,分别分析车辆在黄灯期间的驾驶行为.根据车辆行驶速度、与停车线的距离这2个因素,以Spss为分析工具,利用二元Logistic模型,建立了车辆在黄灯期间的驾驶行为模型.其中基于时间变量建立的模型精度为15.9%,由模型得出其犹豫区的时间分布为1.89~4.86 s. 相似文献
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章对城市交叉口信号配时问题进行了研究.从行车和行人的安全角度出发.研究了6相位的控制方式,提出了以车辆在交叉口的时间延误之和最小化为控制目标.建立了一个新的交叉口信号配时模型,并采用了神经网络、遗传算法等人工智能方法对模型进行求解。 相似文献
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讨论了多相位控制条件下交叉口的周期和绿信比优化模型.并将其转化为一个带约束的多变量函数求极值问题;而后在满足交通控制所需精度的条件下提出求解该模型的改进“爬山”算法;最后,将模型与算法嵌入到交通设计与配时优化计算机辅助系统TJSIG中.结合厦门试验工程对模型算法进行了验证。 相似文献
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采用停车视距原理,阐述了机动车绿灯间隔时间在信号控制交叉口的作用,提出了机动车绿灯间隔时间的计算方法,并通过1个简单算例进行说明。最后,给出了绿灯间隔时间在黄灯和全红之间的分配方式。 相似文献
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信号控制下交叉口延误计算方法研究 总被引:19,自引:3,他引:19
为了对交通信号控制参数进行优化,需要对交叉口延误进行定量的分析与计算。根据信号控制交叉口理论,在以往定时信号延误研究的基础上,基于交叉口一个进口方向的车辆延误分析,针对交叉口各进口方向同时处于非饱和与同时处于过饱和交通状况,分析并推导了交叉口延误公式.并用具体的算例说明了公式的用法。公式表明了交叉口延误与信号控制参数、车辆到达率等参数之间的动态关系,为进一步研究交通信号自适应控制方法和建立交通信号控制参数优化的性能指标函数提供了信息。 相似文献
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高速公路施工区信号汇入控制研究 总被引:1,自引:0,他引:1
为减少高速公路施工区由于车道封闭而形成的局部瓶颈路段,使得行驶车辆安全平稳通过施工区,在国内外高速公路施工区汇入控制策略研究的基础上,结合交通流理论对信号汇入控制进行分析和优化,并利用交通仿真技术对比分析常规汇入控制和信号汇入控制效果,研究发现信号汇入控制可以显著提高施工区单位时间的交通通过量,降低每辆车的平均延误和平... 相似文献
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Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks. 相似文献
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Chao Wang 《智能交通系统杂志
》2016,20(5):428-437
》2016,20(5):428-437
Real-time traffic flow forecasting is of great importance in the development of advanced traffic management systems and advanced traveler information systems. Traffic flow is evaluated using time series, and the Autoregressive Integrated Moving Average (ARIMA) model has been commonly used for determining the regression-type relationship between historical and future data. However, the performance of the ARIMA model is limited by the difficulty of capturing nonlinear patterns and the challenges of diagnosing permanent white noises. Hence, a hybrid method of ARIMA-EGARCH-M-GED was developed with the intent to address those limitations. It combines the linear ARIMA model with a nonlinear model of Exponent Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) to capture heteroscedasticity (the variance of random error varying across the data) of traffic flow series. EGARCH in Mean (EGARCH-M), which corrects the expression of conditional variance by connecting the conditional mean directly with the variance, was introduced to better restrain the influence of abnormal data. Moreover, the tail of the generalized error distribution (GED) is better than that of the normal distribution in characterizing the features of time series, especially heteroscedasticity of residual sequences. Data collected from an interstate highway (I-80 in California) with a sampling period of 5 minutes were used to evaluate the performance of the proposed model. The results from the hybrid model were compared with ARIMA, an artificial neural network, and a K-nearest neighbor model. The results showed that the hybrid model outperformed the other methods in terms of accuracy and reliability. Overall, the proposed model performed well in tracking the features of measured data and controlling the impact of abnormal data. 相似文献
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交通影响评价中的交通区分割方法研究 总被引:1,自引:2,他引:1
为了在交通影响分析中准确预测道路网络的宏观交通流和局部路口的微观交通流特征,开发了利用交通量分配模型和交通仿真技术组合分析交通影响的方法。首先在地理信息系统(GIS)的支持下,利用Voronoi图对交通小区进行几何分割,并借助GIS的空间聚合分析功能分割相应的属性数据,然后利用MCI(Multiplicative competitive interaction)模型推求新OD矩阵。从而使交通需求预测模型的输出结果可以直接作为交通仿真的初始数据,以便利用交通仿真技术实施关键路口的微观交通流分析。最后通过一个试验验证了方法的有效性和精度。 相似文献