In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (e.g., paid parking or bus stop), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods that optimize for a given curb zoning value function. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that we solve in parallel; this decomposition accelerates the MIP solver considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Optimal, centralized dynamic curbside parking space zoning


    Beteiligte:
    Nazir, Nawaf (Autor:in) / Dowling, Chase (Autor:in) / Choudhury, Shushman (Autor:in) / Zoepf, Stephen (Autor:in) / Ma, Ke (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    1584447 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Curbside parking time limits

    Arnott, Richard / Rowse, John | Elsevier | 2013


    Curbside parking time limits

    Arnott, Richard | Online Contents | 2013


    Parking Reservation for Managing Downtown Curbside Parking

    Chen, Zhibin / Yin, Yafeng / He, Fang et al. | Transportation Research Record | 2015



    Curbside Parking Monitoring With Roadside LiDAR

    Chen, Zhihui / Xu, Hao / Zhao, Junxuan et al. | Transportation Research Record | 2023