On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.
复制标题
按需通过动态跳动车辆分配的按需高容量骑行共享。
DOI：
10.1073/pnas.1611675114
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发表时间：
2017-01-17
影响因子：
11.1
通讯作者：
Rus D
中科院分区：
文献类型：
--
作者：
Alonso-Mora J;Samaranayake S;Wallar A;Frazzoli E;Rus D
乘车共享服务不仅可以提供非常个性化的移动性体验，而且还可以通过大规模乘坐乘坐效率和可持续性。实时，当前解决方案未完全解决一项任务。出租车数据和一支乘客的共享车队最多可达我们的结果。 2.8分钟，平均行程延迟为3.5分钟。
乘车共享服务通过向任何人，任何时间提供及时，方便的运输来改变城市移动性，这些服务在污染，能源消耗，拥堵等方面具有巨大的社会影响。一项大规模研究不完全解决乘车共享的潜力。车辆（最佳）或三个（具有启发式方法）。在线需求和车辆位置。随着时间的流逝，我们通过实验量化了舰队的大小，容量，等待时间，旅行延迟和中等容量汽车的运营成本，例如出租车和货车。纽约市的出租车公共数据集。我们的实验研究认为骑车人数最多可容纳10个简单的密码。自动驾驶汽车，还将空转车辆的重新平衡到需求量很高。
Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min. Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.