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Models for anticipating non-motorized travel choices, and the role of the built environment

机译：预测非机动出行选择的模型以及建筑环境的作用

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This paper uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike+walk) travel modes. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment (BE) variables. Marginal effects of covariates for models of vehicle ownership levels, intrazonal trip-making, destination and mode choices, non-motorized trip counts per household, and miles traveled (both motorized and non-motorized) are presented. Mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip purposes, to recognize the distinct behaviors at play when making shorter versus longer trips and serving different activities. The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus-stop density, and greater non-motorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Intrazonal trip likelihoods rose with street connectivity, transit availability, and land use mixing. Across all BE variables tested, street structure offered the greatest potential behavioral impacts, alongside accessibility indices (for both motorized and non-motorized access). For example, non-motorized trip counts are estimated to rise 26% following a one standard deviation increase in this variable, and walk probabilities by 27% following a one standard deviation increase in this index at the destination zone. Regional and local accessibility and density (of population plus jobs) variables were also important predictors, depending on the response being modeled. Simulated model applications illuminate when and to what extent significant travel behavior changes may be witnessed, as land use settings and other variables are changed, to reflect existing neighborhoods.
机译：本文使用西雅图市区的详细旅行数据来评估建筑环境变量对非机动（自行车+步行）旅行模式的使用的影响。几种模型规范用于根据家庭，人和建筑环境（BE）变量来理解和解释非机动出行行为。提出了车辆拥有量模型，区域内出行，目的地和方式选择，每个家庭的非机动出行次数以及行驶里程（机动化和非机动化）的协变量的边际效应。模式和目的地选择模型分别针对区域间和区域内旅行以及三个不同旅行目的中的每一个进行了估计，以识别短途旅行和长途旅行并服务于不同活动的不同行为。结果强调了街道连通性的重要性（量化为半英里半径内的3路和4路交叉路口的数量），更高的公交车站密度和更大的非机动车出入对于降低车辆拥有率的重要性（之后控制家庭规模，收入，社区密度等），更高的非机动出行率（每天）和非机动方式选择的可能性更高。区域内旅行的可能性随着街道的连通性，公共交通的便利性和土地用途的混合而增加。在所有测试的BE变量中，街道结构与可及性指数（机动和非机动出入）一起提供了最大的潜在行为影响。例如，在此变量中，非机动旅行计数估计值随该标准偏差增加一个标准偏差而增加26％，而在此指标处该索引增加一个标准偏差之后，步行概率将增加27％。区域和本地的可及性以及（人口与工作的密度）变量也是重要的预测指标，具体取决于所建模的响应。模拟的模型应用程序说明了随着土地用途设置和其他变量的变化，可以反映出现有邻域的显着旅行行为何时以及在何种程度上可以看到。

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