Fundamental performance limits and efficient polices for Transportation-On-Demand systems

Abstract

Transportation-On-Demand (TOD) systems, where users generate requests for transportation from a pickup point to a delivery point, are already very popular and are expected to increase in usage dramatically as the inconvenience of privately-owned cars in metropolitan areas becomes excessive. Routing service vehicles through customers is usually accomplished with heuristic algorithms. In this paper we study TOD systems in a formal setting that allows us to characterize fundamental performance limits and devise dynamic routing policies with provable performance guarantees. Specifically, we study TOD systems in the form of a unit-capacity, multiple-vehicle dynamic pickup and delivery problem, whereby pickup requests arrive according to a Poisson process and are randomly located according to a general probability density. Corresponding delivery locations are also randomly distributed according to a general probability density, and a number of unit-capacity vehicles must transport demands from their pickup locations to their delivery locations. We derive insightful fundamental bounds on the steady-state waiting times for the demands, and we devise constant-factor optimal dynamic routing policies. Simulation results are presented and discussed.

Autonomous Mobility-on-Demand Systems for Future Urban Mobility

“…By removing in an environment, one transforms the problem of controlling N different queues into one and control, and allows one to derive analytical expressions for important design parame- is presented in Section 19.3. With this approach, it is also possible to obtain formal performance bounds (i.e., factors of sub-optimality) for receding horizon control policies, in the asymptotic regimes (heavy-load, system saturated) and + (light-load, system empty of customers) [33,17].…”

Robotic load balancing for mobility-on-demand systems

“…DPDPs can be divided into three main categories (Berbeglia et al, 2010): (i) Dynamic Stacker Crane Problem, where the vehicles have unit capacity, (ii) Dynamic Vehicle Routing Problem with Pickups and Deliveries, where the vehicles can transport more than one request, and (iii) Dynamic Dial-a-Ride Problem, where additional constraints such as time windows are considered. Excellent surveys on heuristics, metaheuristics and online algorithms for DPDPs can be found in Berbeglia et al (2010) and Parragh et al (2008), while analysis specifically tailored to the structural properties of transportation-on-demand systems can be found in Pavone et al (2010). The key difference from DPDP problems is that there are a finite number of pick-up and delivery sites, the vehicles are not aware of the destination of newly arrived customers, and the optimization is over the empty vehicle trips.…”

Autonomous personal vehicle for the first- and last-mile transportation services

“…Certain aspects of fleet management have been considered, e.g., in [10]. In this paper, we consider another main challenge, which is in putting autonomy into the transport vehicles to allow them to operate with minimal human intervention.…”