Traffic-state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision-making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly affect traffic conditions. This paper proposes a multidimensional spatiotemporal graph attention-based traffic-prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane-closure events, temperature, and visibility across a large transportation network. The approach is based on a graph attention network architecture, which learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic-speed and lane-closure data from the Caltrans Performance Measurement System (PeMS) and corresponding weather data from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). The numerical experiments implement three alternative models which do not allow for multidimensional input, along with two alternative multidimensional models, based on the literature. The M-STGAT outperforms the five alternative models in validation and testing with the primary data set, as well as for one transfer data set across all three prediction horizons for all error measures. However, the model’s transferability varies for the remaining two transfer data sets, which may require further investigation. The results demonstrate that M-STGAT has the most consistently low error values across all transfer data sets and prediction horizons.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2024-05-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Networkwide Approach to Optimal Signal Timing for Integrated Transit Vehicle and Traffic Operations

    Koch, M. I. / Chin, D. C. / Smith, R. H. et al. | British Library Conference Proceedings | 1995


    Networkwide Impacts of Vehicle Ecospeed Control in the Vicinity of Traffic Signalized Intersections

    Kamalanathsharma, Raj Kishore / Rakha, Hesham A. / Yang, Hao | Transportation Research Record | 2019


    Optimal Graph Information Fused Graph Attention Network for Traffic Flow Forecasting

    Xing Xu / Luchen Fei / Yun Zhao et al. | DOAJ | 2025

    Free access

    A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting

    Zhang, Shaokun / Guo, Yao / Zhao, Peize et al. | IEEE | 2022