Yazar "Zhu, Pengbo" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    A Coverage Control-Based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Zhu, Pengbo; Sirmatel, Isik Ilber; Ferrari-Trecate, Giancarlo; Geroliminis, Nikolas
    As an emerging mode of urban transportation, autonomous mobility-on-demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services. However, the spatiotemporal imbalances between mobility demand and supply may lead to inefficiencies and a low quality of service. Vehicle rebalancing (i.e., dispatching idle vehicles to high-demand areas) is a potential solution for efficient AMoD fleet management. In this article, we formulate the vehicle rebalancing problem as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas. Performance is demonstrated via microscopic simulations representing a large urban road network in Shenzhen, China. The results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.
  • Küçük Resim Yok
    Öğe
    Idle-vehicle Rebalancing Coverage Control for Ride-sourcing systems
    (IEEE, 2022) Zhu, Pengbo; Sirmatel, Isik Ilber; Trecate, Giancarlo Ferrari; Geroliminis, Nikolas
    Ride-sourcing system can provide passengers with fast and efficient service with a fleet of vehicles, while asymmetry between origin and destination distributions of trips, nonuniform passenger's demand for rides in different districts creates imbalances in the spatial distribution of these vehicles. Thus proactively relocating idle vehicles to the high-demand regions, also known as vehicle rebalancing is an emerging problem that can have a significant improvement for the efficiency of urban transportation. We formulate this problem as a coverage problem for coordination and deployment of multiple mobile agents in city scenarios, which vehicles can benefit from by allocating them according to the different demand densities of different city districts. A Voronoi-based control algorithm is proposed by leveraging the local information of each vehicle. The effectiveness of the proposed method is validated by a simulator modeled on a real road map from Shenzhen, China. Compared to baseline, our proposed method is able to serve more trips with less passenger waiting time.