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DRIVER MANDATORY LANE CHANGE BEHAVIOR: USE OF GOVERNING GAP IN CRITICAL GAP ESTIMATION

机译:驾驶员强制性车道变化行为:在临界间隙估计中使用控制间隙

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This study analyzed the driver gap acceptance and rejection behavior during mandatory lanechanges on a multilane freeway. During a lane change, drivers were more receptive to either theleading or the trailing gaps with vehicles in the target lane which governed the drivers’ lanechange and is termed as the governing gap. Drivers maneuvered till the governing gap wasgreater than the critical gap, then accepted the gap and made a lane change. In this process,drivers reduced the non-governing gap to increase the length of the governing gap. The drivers,as a result, were found to be consistent with respect to the governing gap and inconsistent withrespect to the non-governing gap. The governing gap, therefore, addresses the consistent driverbehavior and avoids categorization of drivers as inconsistent. Consistent driver behavior isfurther augmented in this paper by considering the largest rejected less than the accepted gap(LRLA) as the effective rejected gap. Critical gaps were estimated based on the accepted andLRLA gaps, firstly, by categorizing the drivers based on the governing gap and the type ofmaneuver, and secondly, by categorizing the drivers based on the relative speeds. For a simplelane change model, categorization by governing gap and type of maneuver will be sufficient witha critical gap value distribution defined by empirical data. For a sophisticated lane change model,in addition to maneuver types, critical gaps estimated based on difference in relative speeds willhelp better replicate the realistic lane change behavior of drivers.
机译:这项研究分析了强制性车道上驾驶员差距的接受和拒绝行为 在多车道高速公路上进行更改。在换道期间,驾驶员更容易接受 在目标车道上控制驾驶员车道的车辆的领先差距或落后差距 变化,被称为治理差距。驾驶员一直在操纵,直到达到控制差距为止 大于临界差距,然后接受差距并改变车道。在这个过程中 驱动程序减少了非政府的差距,从而增加了治理差距的长度。司机们 结果,发现在治理差距方面是一致的,并且与 尊重非政府的差距。因此,治理差距可以解决一致的驱动因素 行为并避免将驱动程序分类为不一致的。一致的驱动程序行为是 通过考虑最大的拒绝率小于可接受的差距,进一步扩大了本文的范围 (LRLA)作为有效的拒绝差距。关键差距是根据公认的和 LRLA的差距,首先,根据治理差距和 机动,其次,根据相对速度对驾驶员进行分类。对于一个简单的 车道变更模型,通过控制间隙和操纵类型进行分类就足够了 由经验数据定义的临界间隙值分布。对于复杂的车道变更模型, 除了机动类型外,根据相对速度的差异估算的临界间隙还将 帮助更好地复制驾驶员实际的换道行为。

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