Understanding and modeling route choice decisions

理解路线选择决策并对其进行建模

• 批准号: 261850-2009
• 负责人: Scott, Darren
• 金额: $ 1.38万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571161
• 关键词: Understanding; modeling; route; choice; decisions

Although travel behavior research in general has advanced considerably over the past decade, spearheaded by new data collection technologies and new modeling methods, one area that has seen less work is that pertaining to route choice. In part, this stems from a lack of data pertaining to observed routes - data that can now be collected via GPS technology. At the same time, in an era characterized by rising oil prices, energy conservation, traffic congestion, aging populations and climate change, there is a growing need to improve our understanding of the spatial manifestation of activity/travel behavior - that is, navigating urban transportation networks. Since travel demand forecasting is done using some form of traffic assignment algorithm, the research proposed can reveal whether the assumptions underlying such algorithms are valid. If not, then one must question the validity of predictions from such models - predictions that are used to guide development in urban areas. In other words, how can a transportation planner enhance quality of life when the model outputs are simply inaccurate? The goal of the proposed research is to understand and model route choice decisions. To this end, a comprehensive GPS-enhanced data set collected in 2007-08 for a sample of households in the Halifax Regional Municipality, Nova Scotia, Canada is used to address the following objectives: (1) to compare alternative methods for generating route choice sets; (2) to compare route choice models using alternative modeling structures that have been proposed to capture similarities between routes; (3) to examine the effect of choice set reduction on model estimates; (4) to determine if route choice behavior varies between groups of individuals based on their socio-demographic characteristics; (5) to determine if route choice behavior varies by trip purpose; and (6) to determine if route choice behavior varies by time of day.

尽管在新的数据收集技术和新的建模方法的引领下，出行行为研究在过去十年中总体上取得了相当大的进步，但有一个领域的研究工作较少，那就是与路线选择有关的领域。这在一定程度上是因为缺乏与观测路线有关的数据--这些数据现在可以通过GPS技术收集。与此同时，在一个以油价上涨、节能、交通拥堵、人口老龄化和气候变化为特征的时代，我们越来越需要提高对活动/旅行行为的空间表现形式--即导航城市交通网络--的理解。由于出行需求预测是使用某种形式的交通分配算法进行的，因此所提出的研究可以揭示这些算法所依据的假设是否有效。如果不是，那么人们肯定会质疑这些模型的预测的有效性--这些预测被用来指导城市地区的发展。换句话说，当模型输出完全不准确时，交通规划者如何提高生活质量？ 拟议研究的目标是理解路线选择决策并对其进行建模。为此，2007-2008年收集的加拿大新斯科舍省哈利法克斯地区市政当局家庭样本的综合GPS增强数据集被用来解决以下目标：(1)比较生成路线选择集的替代方法；(2)使用已提出的捕捉路线之间相似性的替代建模结构来比较路线选择模型；(3)检查选择集减少对模型估计的影响；(4)确定个体群体之间的路线选择行为是否基于他们的社会人口特征而变化；(5)确定路线选择行为是否因旅行目的而异；以及(6)确定路线选择行为是否因一天中的不同时间而变化。