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考虑不规则驾驶行为的城市隧道事故率预测 被引量:1
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作者 杜蕊 蔡晓禹 谭静 《科学技术与工程》 北大核心 2020年第7期2892-2898,共7页
准确预测交通事故风险概率是实现隧道内交通安全主动管理的先决条件。从交通状态和驾驶员行为2个方面分析影响事故发生的主要因素,提出“不规则行为率”作为特征变量。对特征变量显著性检验,选用Logistic概率模型构造城市隧道事故率预... 准确预测交通事故风险概率是实现隧道内交通安全主动管理的先决条件。从交通状态和驾驶员行为2个方面分析影响事故发生的主要因素,提出“不规则行为率”作为特征变量。对特征变量显著性检验,选用Logistic概率模型构造城市隧道事故率预测模型,并提出利用接受者操作特性曲线寻找阈值的方法。最后基于实测的青岛胶州湾隧道数据进行实例验证,研究结果表明,阈值确定为0.36较为合适,此时基于不规则驾驶行为的事故率预测模型准确度为78.4%,能够较为准确地预测隧道内事故发生的可能性,研究成果可以为智能交通安全发展奠定理论基础。 展开更多
关键词 城市隧道安全 不规则驾驶行为 Logistic概率模型 事故率预测
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Drivers'fixation transfer characteristics in urban tunnels 被引量:1
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作者 Guo Tangyi Pan Shu +1 位作者 Shao Fei Xu Qian 《Journal of Southeast University(English Edition)》 EI CAS 2021年第3期325-331,共7页
To improve the safety performance of urban tunnels,the fixation transfer characteristics of drivers with different driving experience levels in urban tunnels were investigated.First,a real vehicle test was performed i... To improve the safety performance of urban tunnels,the fixation transfer characteristics of drivers with different driving experience levels in urban tunnels were investigated.First,a real vehicle test was performed in an urban tunnel,and the eye movement data of 10 drivers with different driving experience levels were collected using a Dikablis eye-tracking system.Second,the driver fixation range was divided into eight areas of visual interest by using the K-means clustering method,and the fixations in different sections of the tunnel were comparatively analyzed.Finally,on the basis of the divided areas of visual interest,fixation transfer rules and the stationary distribution characteristics of drivers with different driving experience levels on different sections of the tunnel were discussed using Markov theory.Results indicate that drivers'probability of repeated fixation is greater and that the efficiency of visual search is lower at internal sections of tunnels than in external sections.Drivers obtain information mainly from the straight upper front and straight lower front areas,and the probabilities of fixation points in these two areas at the threshold and exit sections are significantly higher than those in other sections.Relative to experienced drivers,novice drivers allocate little attention to the straight upper front area and rear-view mirrors.Hence,they have weak fixation when looking forward,and they lack experience in obtaining information on rear-approaching vehicles and controlling speed. 展开更多
关键词 traffic safety urban tunnel eye movement fixation transfer Markov theory
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Post-failure behavior of tunnel heading collapse by MPM simulation 被引量:8
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作者 CHENG XueSong ZHENG Gang +4 位作者 SOGA K. BANDARA S.S. KUMAR K. DIAO Yu XU Jie 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2015年第12期2139-2152,共14页
Tunnel collapse presents a serious threat to the safety of urban construction. The traditional approach adopted to assess this risk is to evaluate the factor of safety against failure. However, this analysis only dete... Tunnel collapse presents a serious threat to the safety of urban construction. The traditional approach adopted to assess this risk is to evaluate the factor of safety against failure. However, this analysis only determines on whether the tunnel will collapse or not, and does not provide information on the magnitude of the post-failure behavior(for example, catastrophic or progressive) if the tunnel collapse occurs. In this study, a meshless method based on the material point method(MPM) was used to investigate the post-failure behavior of tunnel heading collapse in two-dimensional plane-strain conditions. The capability and accuracy of MPM were verified by comparing the elicited results to centrifuge test data and to analytical solutions obtained from limit state methods. MPM simulations were conducted at different soil conditions(clay or sand) and profiles(homogenous or linear increasing strength) as well as at different tunnel geometries(i.e. tunnel depth and unlined length). The differences in the post-failure behavior and mechanisms are examined and reported. 展开更多
关键词 tunnel stability collapse mechanism material point method large deformation
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