Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting. These contributions tend to emphasize the modeling of spatial correlations, while neglecting the fairly stable and recurrent nature of human mobility patterns. In this short paper, we show that a naive baseline method based on the average weekly pattern and linear regression can achieve comparable results to many state-of-the-art deep learning approaches for spatio-temporal forecasting in transportation, or even outperform them on several datasets, thus contrasting the importance of stationarity and recurrent patterns in the data with the importance of spatial correlations. Furthermore, we establish 9 different reference benchmarks that can be used to compare new approaches for spatio-temporal forecasting, and provide a discussion on best practices and the direction that the field is taking.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    On the Importance of Stationarity, Strong Baselines and Benchmarks in Transport Prediction Problems


    Contributors:


    Publication date :

    2023-09-24


    Size :

    306076 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Training for Inter-Rater Reliability: Baselines and Benchmarks

    Williams, D. M. / Holt, R. W. / Boehm-Davis, D. A. | British Library Conference Proceedings | 1997


    CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

    Gadre, Samir Yitzhak / Wortsman, Mitchell / Ilharco, Gabriel et al. | ArXiv | 2022

    Free access

    Efficient Baselines for Motion Prediction in Autonomous Driving

    Gomez-Huelamo, Carlos / Conde, Marcos V. / Barea, Rafael et al. | IEEE | 2024


    Baselines for Image Annotation

    Makadia, A. / Pavlovic, V. / Kumar, S. | British Library Online Contents | 2010


    Comparative Space Power Baselines

    Millis, Marc G. / Davis, Eric W. | AIAA | 2009