arXiv 1609.025462016-09-15eess.SYcs.SY

考虑拥堵的自主出行需求系统中的随机路由

Congestion-Aware Randomized Routing in Autonomous Mobility-on-Demand Systems

Federico Rossi, Rick Zhang, Marco Pavone

备注：: This paper has been withdrawn by the authors due to an error in the proofs of Theorem 3.4 (bound on the probability of violating the congestion constraints) and Lemma 3.5 (approximation factor of the algorithm)

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AI总结：

本文研究了自主车辆在拥堵城市道路网络中的路由和重新平衡问题，提出了一种随机算法，在多项式时间内找到近似最小化道路上车辆数的低拥堵解决方案，并提供理论界限和实验证明其有效性。

摘要：

本文研究了自主车辆在拥堵城市道路网络中的路由和重新平衡问题。我们证明了无拥堵的路由和重新平衡问题是NP难的，并提供了一种随机算法，能够在多项式时间内找到近似最小化道路上车辆数的低拥堵解决方案。我们提供了违反拥堵约束概率的理论界限，并利用常用的实证拥堵模型characterized解决方案所需的车辆期望数量，并提供了算法近似因子的界。在具有现实客户需求的真实道路网络上的数值实验表明，我们的算法引入了极小的拥堵。在旅行时间和所需车辆数量方面，我们的算法表现非常接近（有时优于）无拥堵解决方案。

英文摘要：

In this paper we study the routing and rebalancing problem for a fleet of autonomous vehicles providing on-demand transportation within a congested urban road network (that is, a road network where traffic speed depends on vehicle density). We show that the congestion-free routing and rebalancing problem is NP-hard and provide a randomized algorithm which finds a low-congestion solution to the routing and rebalancing problem that approximately minimizes the number of vehicles on the road in polynomial time. We provide theoretical bounds on the probability of violating the congestion constraints; we also characterize the expected number of vehicles required by the solution with a commonly-used empirical congestion model and provide a bound on the approximation factor of the algorithm. Numerical experiments on a realistic road network with real-world customer demands show that our algorithm introduces very small amounts of congestion. The performance of our algorithm in terms of travel times and required number of vehicles is very close to (and sometimes better than) the optimal congestion-free solution.

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