Understanding and Modeling Non-motorized Travel Behavior

可持续的城市规划试图通过淡化汽车作为首选出行工具来提高生活质量。实现这一目标的战略包括适合步行的社区、混合使用环境、以交通为导向的开发、密集化、完整的街道和多式联运系统。然而，预测这些战略的影响需要包含所有出行方式的预测模型，而不仅仅是汽车和交通工具。鉴于加拿大的城市规划现在注重可持续性，有必要加深我们对步行和骑自行车的非机动车出行行为的理解，并开发可用于评估上述策略的预测模型。*为了进一步发展这样的模型，拟议的研究有两个相互交织的目标：(1)加强对加拿大非机动车出行行为的理解，(2)解决模型从一个时空背景到另一个时空背景的可转移性问题。为了实现这些目标，我们设计了五个目标：(1)开发分别包含步行和骑自行车的“国家级”工作出行和非工作出行的模式选择模型；(2)开发“国家级”的模式选择模型，分别汇集工作和非工作出行的两个时间段的数据，其中包括步行和骑自行车；(3)开发一个“国家级”出行生成模型，该模型捕获按模式的每日出行次数之间的相互作用；(4)开发一个“国家级”出行生成模型，该模型汇集来自两个时间段的数据，以捕获按模式进行的每日出行次数之间的交互作用；和(5)分别开发步行和骑自行车的路线选择模型。*关于提高知识，拟议的研究将揭示管理非机动车出行的因素(例如，天气条件、建筑环境、人的特征)在多大程度上导致全国范围内相同的行为结果。这项研究还将揭示这些因素的影响是否会随着时间的推移而变化，从而改变行为结果。就实际结果而言，通过这项研究开发的政策敏感模型可以很容易地传播到加拿大各地的城市规划机构，以预测可持续城市规划战略引起的步行和骑自行车需求的变化。这些模型将对目前没有这种预测工具的机构特别有用。总而言之，拟议的研究不仅提高了知识水平，还为开发性价比高、行为合理的旅行需求预测模型提供了一种创新的解决方案。

Sustainable urban planning seeks to enhance quality of life by deemphasizing cars as a preferred means of travel. The strategies for achieving this include walkable neighborhoods, mixed-use environments, transit-oriented development, densification, complete streets, and multi-modal transportation systems. However, predicting the impacts from such strategies requires forecasting models that incorporate all modes of travel, not just cars and transit. Given that urban planning in Canada now focuses on sustainability, there is a timely need to further our understanding of non-motorized travel behavior walking and bicycling and develop forecasting models that can be used to evaluate the strategies listed above.*** To further such model development, the proposed research has two intertwined goals: (1) to enhance the understanding of non-motorized travel behavior in Canada, and (2) to address the issue of model transferability from one spatio-temporal context to another. Five objectives are designed to achieve these goals: (1) develop “national-level” mode choice models for work and non-work trips, respectively, that incorporate walking and bicycling; (2) develop “national-level” mode choice models pooling data from two time periods for work and non-work trips, respectively, that incorporate walking and bicycling; (3) develop a “national-level” trip generation model that captures interactions among the number of daily trips by mode; (4) develop a “national-level” trip generation model pooling data from two time periods that captures interactions among the number of daily trips by mode; and (5) develop route choice models for walking and bicycling, respectively.*** With respect to advancing knowledge, the proposed research will reveal the extent to which factors governing non-motorized travel (e.g., weather conditions, built environments, characteristics of people) lead to the same behavioral outcomes across the country. The research will also reveal whether the impact of such factors changes over time, therefore altering behavioral outcomes. In terms of practical outcomes, the policy-sensitive models developed through this research can be easily disseminated to urban planning agencies across Canada for forecasting changes in walking and cycling demand arising from sustainable urban planning strategies. The models will be especially useful to agencies without such forecasting tools at the present time. To summarize, the proposed research not only advances knowledge, it provides an innovative solution to developing cost-effective travel demand forecasting models that are behaviorally sound.**