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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月18日

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  • Marius Hadry,
  • André Bauer,
  • Robert Leppich,
  • Veronika Lesch,
  • Samuel Kounev

摘要

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.