Travel Time Prediction using Tree-Based Ensembles
This work addresses travel time forecasting for urban mobility planning, but it is incremental as it applies existing methods to a specific dataset with feature engineering.
The paper tackles travel time prediction in urban scenarios using tree-based ensembles on NYC taxi data, showing that adding routing data improves performance and that short-term models require little training data for accurate predictions.
In this paper, we consider the task of predicting travel times between two arbitrary points in an urban scenario. We view this problem from two temporal perspectives: long-term forecasting with a horizon of several days and short-term forecasting with a horizon of one hour. Both of these perspectives are relevant for planning tasks in the context of urban mobility and transportation services. We utilize tree-based ensemble methods that we train and evaluate on a dataset of taxi trip records from New York City. Through extensive data analysis, we identify relevant temporal and spatial features. We also engineer additional features based on weather and routing data. The latter is obtained via a routing solver operating on the road network. The computational results show that the addition of this routing data can be beneficial to the model performance. Moreover, employing different models for short and long-term prediction is useful as short-term models are better suited to mirror current traffic conditions. In fact, we show that accurate short-term predictions may be obtained with only little training data.