There is growing support to improve the quality of the walking environment and make investments to promote pedestrian travel. Such efforts often require analytical non-motorized planning tools to estimate levels of pedestrian demand that are sensitive to environmental and demographic factors at an appropriate scale. Despite this interest and need, current forecasting tools, particularly regional travel demand models, often fall short. Until recently, spatially disaggregate travel behavior data on walking activity and detailed data on the quality of the pedestrian environment have been generally unavailable. Recent work by the authors attempts to utilize a new availability of data to develop robust pedestrian planning tools for use in regional travel demand modeling. A project through the Oregon Transportation Research and Education Consortium (OTREC), in partnership with Portland’s metropolitan planning organization Metro, developed a pedestrian modeling framework for use in four-step travel demand models. It constructed models to predict the number of walk trips generated with spatial acuity, utilizing a new measure of the pedestrian environment and a micro-level unit of analysis. This National Institute for Transportation and Communities (NITC) project builds off of the successes of the authors’ previous work, continues the collaboration with Metro, and extends the pedestrian demand estimation tool’s functionality to encompass pedestrian destination choice. Specifically, this project developed statistical models of pedestrian choice behavior, predicting the distribution of walk trips generated (from the previous project) to destinations also at a small spatial scale. Using about 4,500 walk trips from a 2011 household travel survey in the Portland region—the Oregon Household Activity Survey (OHAS)—multinomial logit pedestrian destination choice models were estimated for six trip purposes. Independent variables included terms for walk-trip distance; employment by type; households; supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE); barriers to walking (terrain, freeways, industrial-type employment); and traveler characteristics. Destination alternatives were uniform square analysis zones with quarter-mile (400m) sides. Results suggest important behavioral influences on walking. Distance was a significant deterrent to pedestrian destination choice, and people in carless or childless households were less sensitive to distance for some purposes. Employment (especially retail) was a strong attractor: doubling the number of jobs nearly doubled the odds of choosing a destination for home-based shopping walk trips. More attractive pedestrian environments were also positively associated with pedestrian destination choice after controlling for other factors.