Comparative Evaluation of Tree-Based Ensemble Algorithms for Short-Term
  Travel Time Prediction
  Disseminating accurate travel time information to road users helps achieve
traffic equilibrium and reduce traffic congestion. The deployment of Connected
Vehicles technology will provide unique opportunities for the implementation of
travel time prediction models. The aim of this study is twofold: (1) estimate
travel times in the freeway network at five-minute intervals using Basic Safety
Messages (BSM); (2) develop an eXtreme Gradient Boosting (XGB) model for
short-term travel time prediction on freeways. The XGB tree-based ensemble
prediction model is evaluated against common tree-based ensemble algorithms and
the evaluations are performed at five-minute intervals over a 30-minute
horizon. BSMs generated by the Safety Pilot Model Deployment conducted in Ann
Arbor, Michigan, were used. Nearly two billion messages were processed for
providing travel time estimates for the entire freeway network. A Combination
of grid search and five-fold cross-validation techniques using the travel time
estimates were used for developing the prediction models and tuning their
parameters. About 9.6 km freeway stretch was used for evaluating the XGB
together with the most common tree-based ensemble algorithms. The results show
that XGB is superior to all other algorithms, followed by the Gradient
Boosting. XGB travel time predictions were accurate and consistent with
variations during peak periods, with mean absolute percentage error in
prediction about 5.9% and 7.8% for 5-minute and 30-minute horizons,
respectively. Additionally, through applying the developed models to another
4.7 km stretch along the eastbound segment of M-14, the XGB demonstrated its
considerable advantages in travel time prediction during congested and
uncongested conditions.
