Previous studies on connected autonomous vehicles (CAVs) examined pavement performance and lane widths separately and in isolation, and without consideration for roadworks conditions. Hence, this study presents a holistic, optimal highway design solution for connected autonomous trucks (CATs) by testing pavement failure and traffic performance under different cross-sectional configurations incorporating a dedicated CAT lane for both normal and temporary traffic management (TTM) arrangements. Firstly, a dual three-lane motorway (D3M) was selected as a base case site. Next, previous research on substandard lanes was used to produce five nonstandard cross-section alternatives, which were then modeled using commercially available software. Capital investments to implement the alternatives were calculated by applying established industry construction cost models. Each cross-section was then subjected to different CATs penetration rates (PRs) and wheel wander regimes, and their pavement structural deterioration analyzed using the Texas Mechanistic-Empirical Asphalt Concrete Pavement Design and Analysis System (TxME) software. From this, maintenance frequencies and costs were determined. The study estimated delays and delay costs during TTM over a 20-year design period. Finally, initial investment, rehabilitation and delay costs were combined. It was found that the lowest life-cycle cost (LCC) of £19,091,470 occurred for high (80%) CAT PR operating under Standard D3M, whereas the highest LCC of £152,728,100 was also for high PR, but under Substandard D4M. Optimal LCC was found to change with different PRs. Hence cross-sections should be dynamically modifiable, given the anticipated gradual increase in PRs over time.


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    Title :

    Redesigning Highway Infrastructure Systems for Connected Autonomous Truck Lanes


    Additional title:

    J. Transp. Eng., Part A: Systems


    Contributors:


    Publication date :

    2022-12-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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