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首页> 外文期刊>Journal of Transportation Engineering >Identifying Factors Affecting the Time Taken for Drivers to Complete Freeway Merging Maneuvers under Varying Weather, Traffic, and Geometric Conditions
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Identifying Factors Affecting the Time Taken for Drivers to Complete Freeway Merging Maneuvers under Varying Weather, Traffic, and Geometric Conditions

机译：识别影响驾驶员占用的因素，以完成不同的天气，交通和几何条件下的高速公路合并机动

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摘要

Inadequate understanding of driver heterogeneity and characteristics creates challenges building models in microscopic traffic simulation tools that accurately represent merging behavior in real traffic. To further understand merging behavior, this study, unlike previous studies, considers age group and other driver characteristics under varying weather, traffic, and geometric conditions. A pilot study was conducted using a driving simulator to simulate merging scenarios on four-lane and six-lane freeway segments for Level of Service (LOS) A and B under clear and foggy weather conditions. A total of 100 individuals voluntarily participated in the study and their time taken to complete merging maneuvers (or merging time) was used as the performance measure. The collected data were analyzed using ANOVA and log-linear regression models, and results show that although there were statistically significant differences among age groups, "number of lanes" was the most significant predictor variable in the model because drivers required longer time merging to the four-lane freeway segment than to the six-lane freeway segment. Also, some driver characteristics and self-reported driving abilities were found to influence the merging time of drivers.
机译：理解驾驶员异质性和特征不足，创造了在远程交通中准确表示合并行为的微观交通仿真工具中的建立模型的挑战。为了进一步了解合并行为，本研究与以前的研究不同，考虑到不同天气，交通和几何条件下的年龄组和其他驾驶员特征。使用驾驶模拟器进行试验研究，以模拟四车道和六车道高速公路段的合并情景，以实现在清晰和有雾的天气条件下的服务（LOS）A和B水平。总共有100名自愿参与研究及其在完成合并机动（或合并时间）的时间作为绩效措施。使用ANOVA和Log-Linear回归模型进行分析收集的数据，结果表明，虽然年龄组之间存在统计学上显着差异，但“车辆数量”是模型中最重要的预测因子变量，因为司机需要更长的时间合并四车道高速公路段而不是六车道高速公路段。此外，发现一些司机特征和自我报告的驾驶能力影响了司机的合并时间。

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