Travel Time Prediction using Tree-Based Ensembles

TL;DR

This paper explores travel time prediction in urban areas using tree-based ensemble models, considering both short-term and long-term horizons, and highlights the importance of feature engineering and model selection for accuracy.

Contribution

It introduces a dual approach for short-term and long-term travel time forecasting using tree-based ensembles with feature engineering from routing and weather data.

Findings

Routing data improves model performance

Short-term models require less training data

Different models suit different prediction horizons

Abstract

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.