基于出行链的过度通勤建模与分析
Modeling and Analysis of Excess Commuting with Trip Chains
摘要：
像其他类型的人类旅行一样，通勤在本质上是复杂的，例如行程链行为涉及在两个锚点之间进行多个目的的停留。根据2001年全国家庭旅行调查，大约一半的美国工作人员在工作日通勤时会停下来。然而，在研究一个地区整体通勤效率的过度通勤研究中，通勤被简化为从家到工作的不间断旅行。本研究填补了这一空白，提出了一个基于出行链的模型，将出行链行为整合到过度通勤。基于对佛罗里达州坦帕湾地区的案例研究，本研究发现，传统的过度通勤研究低估了实际和最佳通勤，而高估了过度通勤。例如，仅就连锁通勤出行而言，考虑到出行连锁，平均最短通勤时间从5.48分钟增加到9.32分钟，增加了70%。的差距，发现不同类型的链活动的分类分析，在旅行链类型。因此，政策制定者和规划者在制定城市交通和土地利用政策时应注意忽略出行链行为。此外，该模型还可用于研究非工作出行的效率。
英文摘要：
Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about one half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region's overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned of omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of non-work travel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
