Development of a Multi-class Bicyclist Route Choice Model Using Revealed Preference Data

Existing regional travel forecasting systems are not typically set up to forecast usage of bicycle infrastructure and are insensitive to bicyclists' route preferences in general. We collected revealed preference, GPS data on 162 bicyclists over the course of several days and coded the resulting trips to a highly detailed bicycle network model. We then use these data to estimate bicyclist route choice models. As part of this research, we developed a sophisticated choice set generation algorithm based on multiple permutations of labeled path attributes, which seems to out-perform comparable implementations of other route choice set generation algorithms. The model was formulated as a Path-Size Logit model to account for overlapping route alternatives. The estimation results show compelling intuitive elasticities for route choice attributes, including the effects of distance and delay; avoiding high-volumes of vehicular traffic, stops and turns, and elevation gain; and preferences for certain bike infrastructure types, particularly at bridge crossings and off-street paths. Estimation results also support segmentation by commute versus non-commute trip types, but are less clear when it comes to gender. The final model will be implemented as part of the regional travel forecasting system for Portland, Oregon, U.S.A. 1. Introduction Non-motorized travel options have been largely ignored in regional transportation planning studies in the U.S., where decisions on more resource-intensive investments in highway and transit facilities have been of primary concern. Recently, however, policy-maker interest in sustainable transportation systems and healthier lifestyles has shifted some of the decision-making focus to bicycling and walking and the extent to which the urban travel environment supports these alternative modes. Travel forecasting models, the " workhorse " analytical tool for regional transportation decision making, have been well-developed to reflect the attributes important to predicting traveler responses to changes in the level of service of highway systems and transit systems, but are startlingly unrealistic in their representation of the pedestrian and bicycling environment. Focusing on bicycling, the practice implemented in all known operational travel forecasting models used in North America has been to assume that riders choose the minimum-distance path between origins and destinations using a fixed travel speed, usually without consideration of network attributes. Congestion effects and other travel environment attributes, such as elevation and the presence of dedicated bike lanes, separated bikeways, or "bike boulevards" are not considered. Moreover, extant models do not differentiate between classes of bicycle riders, nor do they segment the travel market in ways …