Abstract:
Travel time prediction is crucial in developing mobility
on demand systems and traveller information systems. Precise
estimation of travel time supports the decision-making process
for riders and drivers who use such systems. In this paper, static
travel time for taxi trip trajectories is predicted by applying
isolated XGBoost regression models to a set of identified inlier
and extreme-conditioned trips and the results are compared
with other existing best models in this context. XGBoost uses an
ensemble of decision trees and is robust to outliers and thus it
is believed to perform well on time series predictions. We show
that, compared to other existing best models, XGB-IN (XGBoost
prediction model of in-lier trips) model prediction values reduce
mean absolute error as well as root mean squared error and
exhibit impressive correlation with actual travel time values
while XGB-Extreme model is able to provide reasonably accurate
prediction results for a set of extreme-conditioned trips with
shorter actual time durations. We demonstrate the achievability
of travel time prediction with XGBoost regression and show that
our approach is applicable to large-scale data and performs well
in predicting static travel time.
