Using an integrated land use and travel model system implemented for the Puget Sound region in Washington state, a Bayesian Melding technique is applied to represent variations in land use outcomes, and is propagated into travel choices across a multi-year agent-based simulation. A scenario is considered where zoned capacity is increased around light rail stations. Samples are drawn from the posterior distribution of households to generate travel model inputs. They allow for propagation of land use uncertainty into travel choices, which are themselves assessed for uncertainty by comparing against observed data. Resulting travel measures of zonal vehicle miles traveled (VMT) per capita and light rail station boardings indicate the importance of comparing distributions rather than point forecasts. Results suggest decreased VMT per capita in zones near light rail stations and increased boardings at certain stations with existing development, and less significant impacts around stations with lower initial development capacity. In many cases, individual point level comparisons of scenarios would lead to very different conclusions. Altogether, this finding adds to a line of work demonstrating the policy value of incorporating uncertainty in integrated models and provides a method for assessing these variations in a systematic way.

中文翻译：

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交通预测中的土地利用不确定性

使用为华盛顿州普吉特海湾地区实施的综合土地利用和旅行模型系统，贝叶斯融合技术被应用于表示土地利用结果的变化，并在基于代理的多年模拟中传播到旅行选择中。考虑增加轻轨站周围的分区容量的情况。从家庭的后验分布中抽取样本以生成旅行模型输入。它们允许将土地使用的不确定性传播到旅行选择中，这些选择本身通过与观察到的数据进行比较来评估不确定性。由此产生的人均区域车辆行驶里程 (VMT) 和轻轨站上车的旅行测量表明比较分布而不是点预测的重要性。结果表明，轻轨站附近区域的人均 VMT 减少，某些现有开发的车站的上车人数增加，而初始开发能力较低的车站周围的影响较小。在许多情况下，情景的个别点级比较会导致非常不同的结论。总而言之，这一发现增加了一系列工作，证明了将不确定性纳入综合模型的政策价值，并提供了一种以系统方式评估这些变化的方法。