Abstract In recent years, ramp controls and advanced freeway designs to improve freeway traffic operations have received considerable attention. Concurrently, work has been undertaken to develop coordinated design and/or control systems to improve street operations. However, unless an integrated approach to design and/or control of both freeway and the adjacent street network (Urban Freeway Corridor) is developed, an effective balancing of traffic against available capacity cannot be attained. The main thrust of the present work is twofold. It determines both control improvement strategies (signal setting and ramp metering plans) and resulting assignment equilibria. The control strategies are selected so that they attempt to reduce peak-hour congestion on the freeway without creating significant delays to motorists on the surface streets. Since alternate routes are available, an original traffic assignment procedure is employed to predict how corridor drivers will react to the above control strategies. This procedure is an extention of the user-optimized traffic assignment algorithm. The equilibrium between individual travel time and assignment is considered. The present methodology combines two existing simulation models with a decision model. The simulation models are deterministic and macroscopic and they predict, respectively, freeway and street traffic performances as functions of: (1) freeway and street designs, and (2) allowable ramp inflows and street link flows. The decision model selects the corridor control settings and predicts the outcoming traffic assignment. It is expected that the results of this research will provide traffic engineers and highway officials with a beneficial tool to be used for evaluating corridor performances and assignment equilibria under different control alternatives. It will also encourage improved usage of existing facilities.


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    Title :

    Evaluation of Freeway Corridor Assignment Equilibria


    Contributors:


    Publication date :

    1976-01-01


    Size :

    24 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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