A key assumption for the successful deployment of mesoscopic transportation network simulation to assess network behavior and performance at a future planning horizon is that the parameters of the constituent macroscopic traffic stream models remain stable at relatively long planning time scales. However, no research has been undertaken to test this assumption systematically. Assessing the validity of this implicit assumption requires rigorous analysis based on empirical traffic data covering a long period. Extensive freeway management systems have now been in place long enough to provide these empirical data. A method to investigate the issue of temporal stability of freeway macroscopic traffic stream models rigorously with 15 years of traffic data from the London Orbital Motorway is presented and implemented. Two forms of macroscopic traffic stream models, namely, a two-regime and a three-regime model form, were studied in this research. With a 13-week, outlier-corrected moving window sampling method and maximum likelihood parameter estimation, time series were developed for the model parameters. The temporal stability characteristics and correlation properties of the fitted parameters are analyzed and discussed. The parameters were found to be relatively stable over time and did not exhibit statistically significant seasonal correlation. Parameter variability was found to be governed primarily by the relative scatter of heavily congested observations and how well the heavily congested regime was represented in the individual data samples. Therefore, modelers should ensure that traffic stream model calibration data sets are free from outliers and well distributed across the model regimes.


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    Titel :

    Temporal Stability of Freeway Macroscopic Traffic Stream Models


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:


    Erscheinungsdatum :

    2012-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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