New vehicle modal emissions rate models will assess emissions as a function of specific operating mode or engine load surrogates. These new models require that vehicle activity be input by fraction of time spent in different operating modes. However, the ability to realistically model onroad modal vehicle activity currently limits the implementation of these models. Few data on how vehicles operate in a real-world setting exist. Simulation models offer attractive advantages for modal modeling. They are readily available and generally can be used with both simple and detailed data input. Simulation models were developed to model the impacts of signal timing, incidents, or design features on traffic flow and perform well for these applications. However, simulation models, such as CORSIM, use theoretical profiles of vehicle acceleration and speed relationships that have not been validated in the field. To determine the feasibility of using simulation models to predict on-road speedacceleration profiles and to identify potential problems in their use as such, a study intersection was modeled in NETSIM, and the simulation output was compared with data collected from field studies of signalized intersections. Analyses of the simulation output and field data indicate that NETSIM does not adequately simulate instantaneous modal vehicle activity. NETSIM intersection activity shows higher fractions of hard accelerations [≥ 9.7+ km/h/s (6 mph/s)] than are demonstrated by field data for the study intersection. For midblock, the results indicate that field data demonstrate a much greater distribution of speeds and accelerations than the distribution modeled by NETSIM.


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    Title :

    Comparison of Speed-Acceleration Profiles from Field Data with NETSIM Output for Modal Air Quality Analysis of Signalized Intersections


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    1999-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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