On-demand Robotic Fleet Routing in Capacitated Networks with Time-varying Transportation Demand
In large-scale automated mobility-on-demand systems, the fleet manager is able to assign routes to individual automated vehicles in a way that minimizes formation of congestion. We formalize the problem of on-demand fleet routing in capacitated networks with time-varying demand. We demonstrate the limits of application of the steady-state flows approach in systems with time-varying demand and formulate a linear program to compute congestion-free routes for the vehicles in capacitated networks under time-varying demand. We evaluate the proposed approach in the simulation of a simplified, but characteristic illustrative example. The experiment reveals that the proposed routing approach can route 42% more traffic in congestion-free regime than the steady-state flow approach through the same network.
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On-demand Robotic Fleet Routing in Capacitated Networks with Time-varying Transportation Demand
Semantic Scholar · Engineering · 2021
Abstract
In large-scale automated mobility-on-demand systems, the fleet manager is able to assign routes to individual automated vehicles in a way that minimizes formation of congestion. We formalize the problem of on-demand fleet routing in capacitated networks with time-varying demand. We demonstrate the limits of application of the steady-state flows approach in systems with time-varying demand and formulate a linear program to compute congestion-free routes for the vehicles in capacitated networks under time-varying demand. We evaluate the proposed approach in the simulation of a simplified, but characteristic illustrative example. The experiment reveals that the proposed routing approach can route 42% more traffic in congestion-free regime than the steady-state flow approach through the same network.
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