Telling fortunes? Evaluation of traffic forecasting models using traffic and context features

Abstract The need for efficient and reliable logistics solutions has increased significantly in the last decade. Traffic forecasts are a promising source of information that can be used to improve the planning of delivery schedules. However, most existing traffic forecasting approaches only support a forecasting horizon of up to an hour, which is insufficient for per-day-based schedule planning. In this paper, we focus on short-term traffic forecasting for up to four hours. We first propose a data collection process integrating traffic speed, incidents, weather, and holiday information. We have used this process to collect real-world traffic data for 115 days. We then define and evaluate twelve models for vehicle traffic forecasting, including well-known time series forecasting approaches and state-of-the-art deep learning models. Our results show that the best model in our comparison improved the accuracy by approximately 30% compared to a naive forecaster that repeats the last known value. The evaluation also shows that LSTM-based approaches are competitive to state-of-the-art models. Overall, the proposed deep-learning-based models perform best while requiring a smaller input timeframe than statistical models.