Prediction of the Deviation between Alternative Routes and Actual Trajectories for Bicyclists
Prediction of the Deviation between Alternative Routes and Actual Trajectories for Bicyclists
Recently published:
Wang, Haotian, Emily Moylan, and David M. Levinson (2022) “Prediction of the Deviation between Alternative Routes and Actual Trajectories for Bicyclists.” Findings, June. [doi].
This study estimates a panel regression model to predict bicyclist route choice. Using GPS trajectories of 600 trips from 49 participants in spring 2006 in Minneapolis, we calculate deviation, the average distance between alternative routes and actual trajectories, as the dependent variable. Trip attributes, including trip length, Vehicle Kilometres Travelled (VKT), the number of traffic lights per kilometer, and the percentage of bike trails and separated bike lane, are included as independent variables. F-tests indicate that both fixed entity and time effect panel regression models offer better fits than the intercept-only model. According to our results, routes with shorter length and higher share of bike trails tend to have less deviation in their trajectories. Traffic lights per km, VKT, and share of bike lane are not significant at the 95% confidence level in this data set.
