Modeling Cyclists’ Route Choice Based on GPS Data

With the increased emphasis on sustainable transportation, advancements are necessary in the technical methods used in the planning and engineering of investments for nonmotorized modes. This paper used GPS data on cyclists’ activities to estimate a utility or generalized-cost function that reflects cyclists’ evaluation of path alternatives. For 724 cycling trips, path attributes were compiled of the observed cycling path to four feasible but not-chosen alternatives. With two logit formulations, the relative importance of statistically significant path parameters—length, auto speed, grade, and the presence (or absence) of bike lanes—was estimated. Then the predictive powers of the models were tested on 181 trips that were observed in the same data set but were not used to calibrate the model. In the best case, this model correctly predicted the actual path for 65% of these trips; for an additional 13% of trips, the difference in probabilities of selecting the best alternative path and the actual path was less than 5%. These results were interpreted to mean that relatively robust path choice (and ultimately mode choice) models may be generated and included in enhanced multimodal travel forecasting models.