用于动态共乘的空闲车辆重新定位
Idle vehicle repositioning for dynamic ride-sharing
摘要：
在动态共乘系统中，空闲车辆的智能重新定位使服务提供商能够最大限度地提高车辆利用率，并最大限度地减少请求拒绝率以及客户等待时间。在目前的实践中，这项任务往往是分散执行的个别司机。我们提出了一个集中的方法，闲置车辆重新定位的形式预测驱动的重新定位算法。我们的方法的核心部分是一个新的混合整数规划模型，旨在最大限度地覆盖预测的需求，同时最大限度地减少旅行时间的重新定位运动。该模型嵌入到规划服务中，还包含其他相关任务，如车辆调度。我们通过对来自汉堡、纽约市和曼哈顿的真实数据集进行广泛的模拟研究来评估我们的方法。我们测试我们的预测驱动的重新定位方法下完美的需求预测，以及一个天真的预测，并比较它的反应策略。结果表明，我们的算法是适合实时使用，即使在大规模的场景。与反应式算法相比，出行请求的拒绝率平均降低了2.5个百分点，客户等待时间平均减少了13.2%。
英文摘要：
In dynamic ride-sharing systems, intelligent repositioning of idle vehicles enables service providers to maximize vehicle utilization and minimize request rejection rates as well as customer waiting times. In current practice, this task is often performed decentrally by individual drivers. We present a centralized approach to idle vehicle repositioning in the form of a forecast-driven repositioning algorithm. The core part of our approach is a novel mixed-integer programming model that aims to maximize coverage of forecasted demand while minimizing travel times for repositioning movements. This model is embedded into a planning service also encompassing other relevant tasks such as vehicle dispatching. We evaluate our approach through extensive simulation studies on real-world datasets from Hamburg, New York City, and Manhattan. We test our forecast-driven repositioning approach under a perfect demand forecast as well as a naive forecast and compare it to a reactive strategy. The results show that our algorithm is suitable for real-time usage even in large-scale scenarios. Compared to the reactive algorithm, rejection rates of trip requests are decreased by an average of 2.5 percentage points and customer waiting times see an average reduction of 13.2%.
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