Bicycle Route Choice Model Developed from Revealed-Preference GPS Data

Abstract

To better understand bicyclists’ preferences, we used bicycle-mounted GPS units to observe the behavior of 162 bicyclists for several days each. Trip purpose and several other trip-level variables were recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. We then used the 1,449 valid non-exercise trips to estimate a bicycle route choice model. As part of this research, we developed a choice set generation algorithm based on multiple permutations of labeled path attributes, which seemed to out-perform comparable implementations of other route choice set generation algorithms. The choice model was formulated as a Path-Size Logit model to account for overlapping route alternatives. Estimation results are intuitive and suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control, and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways (bicycle boulevards), and bridge facilities. Finally, estimation results support segmentation by commute versus non-commute trip types. The route choice model presented in this paper is currently being implemented as part of the regional travel forecasting system for Portland, Oregon, U.S.A.

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