Understanding and Modeling Non-motorized Travel Behavior

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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.