Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and the road network structure. Although existing studies have proposed indicators of betweenness centrality (BC) integrated into regional spatial interactions, the socio-economic drivers of freeway traffic volume formation have been neglected. More importantly, existing studies have not established a non-linear response relationship among BC, city socio-economic spatial interactions, and road traffic volume, which severely limits the comprehensive quantification of the role of freeway traffic flow drivers. Therefore, this study proposes a freeway traffic volume inference method that integrates spatial interaction to enhance BC. First, the socioeconomic factors of the origin and destination cities are incorporated into the BC indicator to create an enhanced betweenness centrality indicator (ODBC), which quantifies the strength of spatial interactions between cities. Second, a machine learning approach is used to develop the non-linear response relationship between ODBC and freeway traffic flow to accurately infer traffic volume. Finally, utilizing the SHapley additive explanation approach, the role vectors of intercity freeway traffic volume drivers are quantified. Experiments conducted on data from freeway toll stations demonstrate that the proposed method surpasses the baseline method based on BC and weighted by BC considering only the potential destination or origin city attractiveness, with an improvement in R2 of 14%, 4.2%, and 4%, and a maximum reduction in RMSE of 40%, 24.5%, and 26%. The proposed method yields higher accuracy for unknown road segments and is easily interpretable.


    Access

    Download


    Export, share and cite



    Title :

    Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality


    Contributors:
    Beibei Zhang (author) / Shifen Cheng (author) / Peixiao Wang (author) / Feng Lu (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Spatio-temporal Correlations of Betweenness Centrality and Traffic Metrics

    Henry, Elise / Bonnetain, Loic / Furno, Angelo et al. | IEEE | 2019


    Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

    Puzis, Rami / Altshuler, Yaniv / Elovici, Yuval et al. | Taylor & Francis Verlag | 2013


    Betweenness Centrality Measures as Potential Predictors of Crash Frequency

    Nair, Shilpa R. / Abhiram, Manoj K. V. / Bhavathrathan, B. K. | Springer Verlag | 2024



    Freeway Traffic Systems

    Ferrara, Antonella / Sacone, Simona / Siri, Silvia | Springer Verlag | 2018