The premise of ride-sharing is that service providers can offer a discount, so that travellers are compensated for prolonged travel times and induced discomfort, while still increasing their revenues. While recently proposed real-time solutions support online operations, algorithms to perform strategic system-wide evaluations are crucially needed. We propose an exact, replicable and demand-, rather than supply-driven algorithm for matching trips into shared rides. We leverage on delimiting our search for attractive shared rides only, which, coupled with a directed shareability multi-graph representation and efficient graph searches with predetermined node sequence, narrows the (otherwise exploding) search-space effectively enough to derive an exact solution. The proposed utility-based formulation paves the way for model integration in travel demand models, allowing for a cross-scenario sensitivity analysis, including pricing strategies and regulation policies. We apply the proposed algorithm in a series of experiments for the case of Amsterdam, where we perform a system-wide analysis of the ride-sharing performance in terms of both algorithm computations of shareability under alternative demand, network and service settings as well as behavioural parameters. In the case of Amsterdam, 3000 travellers offered a 30% discount form 1900 rides achieving an average occupancy of 1.67 and yielding a 30% vehicle-hours reduction at the cost of halving service provider revenues and a 17% increase in passenger-hours. Benchmarking against time-window constrained approaches reveals that our algorithm reduces the search-space more effectively, while yielding solutions that are substantially more attractive for travellers.

共享乘车的前提是服务提供商可以提供折扣，以便使旅行者在延长旅行时间和引起不适的同时获得补偿，同时仍能增加收入。尽管最近提出的实时解决方案支持在线操作，但迫切需要执行战略性全系统评估的算法。我们提出了一种精确的，可复制的和需求驱动的算法，而不是由供应驱动的算法，用于将行程匹配到共享乘车中。我们仅利用划定搜索范围来吸引有吸引力的共享游乐设施，再加上定向的可共享性多图表示和具有预定节点序列的有效图搜索，可以有效地缩小（否则爆炸）搜索空间，以得出精确的解决方案。提议的基于效用的公式化为差旅需求模型中的模型集成铺平了道路，从而允许进行跨场景的敏感性分析，包括定价策略和监管政策。我们在阿姆斯特丹的一系列实验中应用了所提出的算法，在此过程中，我们根据替代需求，网络和服务设置以及行为下共享性的算法计算对乘车性能进行了系统范围的分析参数。以阿姆斯特丹为例，3000名旅客提供了1900种游乐设施的30％折扣，平均入住率达到1.67，并减少了30％的车时，而服务提供商的收入却减少了一半，乘客时数增加了17％。