Understanding and modeling route choice decisions

尽管在过去十年中，在新的数据收集技术和新的建模方法的引领下，旅行行为研究总体上取得了相当大的进步，但有一个领域的工作却很少，那就是与路线选择有关的研究。在某种程度上，这源于缺乏与观测路线相关的数据，而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的理解，即导航城市交通网络。由于出行需求预测是使用某种形式的交通分配算法完成的，因此所提出的研究可以揭示这些算法背后的假设是否有效。如果不是，那么人们必须质疑这些模型预测的有效性——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？

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?