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Online design of dynamic networks

Duo Wang, Andrea Araldo, Mounim El Yacoubi

TL;DR

Differently from classic VRP methods, that extend vehicle trajectories in isolation, the novelty of this paper is that it introduces a method for the online design of dynamic networks, which enables a structured network of line buses, where complex user journeys are possible, thus increasing system performance.

Abstract

Designing a network (e.g., a telecommunication or transport network) is mainly done offline, in a planning phase, prior to the operation of the network. On the other hand, a massive effort has been devoted to characterizing dynamic networks, i.e., those that evolve over time. The novelty of this paper is that we introduce a method for the online design of dynamic networks. The need to do so emerges when a network needs to operate in a dynamic and stochastic environment. In this case, one may wish to build a network over time, on the fly, in order to react to the changes of the environment and to keep certain performance targets. We tackle this online design problem with a rolling horizon optimization based on Monte Carlo Tree Search. The potential of online network design is showcased for the design of a futuristic dynamic public transport network, where bus lines are constructed on the fly to better adapt to a stochastic user demand. In such a scenario, we compare our results with state-of-the-art dynamic vehicle routing problem (VRP) resolution methods, simulating requests from a New York City taxi dataset. Differently from classic VRP methods, that extend vehicle trajectories in isolation, our method enables us to build a structured network of line buses, where complex user journeys are possible, thus increasing system performance.

Online design of dynamic networks

TL;DR

Differently from classic VRP methods, that extend vehicle trajectories in isolation, the novelty of this paper is that it introduces a method for the online design of dynamic networks, which enables a structured network of line buses, where complex user journeys are possible, thus increasing system performance.

Abstract

Designing a network (e.g., a telecommunication or transport network) is mainly done offline, in a planning phase, prior to the operation of the network. On the other hand, a massive effort has been devoted to characterizing dynamic networks, i.e., those that evolve over time. The novelty of this paper is that we introduce a method for the online design of dynamic networks. The need to do so emerges when a network needs to operate in a dynamic and stochastic environment. In this case, one may wish to build a network over time, on the fly, in order to react to the changes of the environment and to keep certain performance targets. We tackle this online design problem with a rolling horizon optimization based on Monte Carlo Tree Search. The potential of online network design is showcased for the design of a futuristic dynamic public transport network, where bus lines are constructed on the fly to better adapt to a stochastic user demand. In such a scenario, we compare our results with state-of-the-art dynamic vehicle routing problem (VRP) resolution methods, simulating requests from a New York City taxi dataset. Differently from classic VRP methods, that extend vehicle trajectories in isolation, our method enables us to build a structured network of line buses, where complex user journeys are possible, thus increasing system performance.

Paper Structure

This paper contains 19 sections, 6 equations, 8 figures, 3 tables, 1 algorithm.

Figures (8)

  • Figure 1: An example of search tree $Tree(s)$.
  • Figure 2: Perform a new simulation on search tree $Tree(s)$ in Fig. \ref{['fig:Styles/NIPS_fig_1.png']}.
  • Figure 3: A toy-example of TEG $\pazocal{G}$ of the bus network.
  • Figure 4: Taxi zones in Manhattan.
  • Figure 5: Real-time MCTS results with different fleet sizes from 9:00 to 13:00.
  • ...and 3 more figures