Passenger car equivalency (PCE) has been used for decades to convert mixed vehicle traffic into equivalent pure passenger car traffic streams for transportation planning, design, and operational analysis purposes. The term PCE was defined by the Highway Capacity Manual (HCM) in 1965 as an adjustment factor, and many approaches have continued to be developed for refining PCE values for different types of freeway geometry and vehicle distributions since then. This paper proposes an improved data-driven state-dependent passenger car equivalency (PCE–SD) approach, initially for level terrain basic freeway segments that consider average vehicle lengths for different types of vehicles. The term “state-dependent” refers to the space headway ratio at the same speed ranges between long vehicles (trucks), that fall into the same clustered category, and passenger cars. The relatively unique data stream used in this study comes from the Portland Oregon Regional Transportation Archive Listing (PORTAL) online datahub. This rich continuous data stream provides valuable information about traffic-related data at a 20-s aggregation level. Most importantly for this effort, ranges of categorical vehicle lengths are also available. According to analysis results, PCE–SD values estimate the actual passenger car distribution at different speeds and levels of operational performance (levels of service) more accurately compared with the HCM-6 PCEs on level terrain basic freeway segments. The authors propose that a state-dependent passenger car equivalency approach bridges the conversion gap from different types of trucks to passenger vehicles and could be used as an improved supplementary methodology.


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    Title :

    Data-Driven Computation of State-Dependent Passenger Car Equivalency for Multiple Truck Lengths


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2022-05-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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