风景路线现在：有效地解决时间相关的弧形定向问题
Scenic Routes Now: Efficiently Solving the Time-Dependent Arc Orienteering Problem
摘要：
本文将圆弧定向问题（AOP）推广到行程时间和价值收益均依赖于时间的大型道路网络，简称为两重时间依赖AOP或2 TD-AOP。在其原始定义中，NP难定向问题（OP）要求找到一条从源到目的地的路径，使累积价值最大化，同时不超过成本预算。OP和AOP的变体具有许多实际应用，例如移动众包任务（例如，修理和维护或派遣现场工人），各种物流问题（例如，人群控制或控制野火）以及若干游客引导问题（例如，生成旅行推荐或在主题公园中导航）。在所提出的2 TD-AOP中，旅行时间和价值函数被假定为时间相关的。例如，动态价值模型可以对众包任务中的不同奖励或损害控制任务中的不同紧急级别进行建模。我们讨论了这个新的问题，证明了时间依赖性的好处经验，并提出了一个有效的近似解决方案，快速响应系统的优化。我们的方法是第一个时间依赖的AOP的变体进行评估，在一个大规模的，细粒度的，真实世界的道路网络。我们证明了最优解是不可行的，静态问题的解往往是无效的。我们提出了一个近似的动态规划的解决方案，产生有效的路径，是数量级的速度比任何最佳的解决方案。
英文摘要：
This paper extends the Arc Orienteering Problem (AOP) to large road networks with time-dependent travel times and time-dependent value gain, termed Twofold Time-Dependent AOP or 2TD-AOP for short. In its original definition, the NP-hard Orienteering Problem (OP) asks to find a path from a source to a destination maximizing the accumulated value while not exceeding a cost budget. Variations of the OP and AOP have many practical applications such as mobile crowdsourcing tasks (e.g., repairing and maintenance or dispatching field workers), diverse logistics problems (e.g., crowd control or controlling wildfires) as well as several tourist guidance problems (e.g., generating trip recommendations or navigating through theme parks). In the proposed 2TD-AOP, travel times and value functions are assumed to be time-dependent. The dynamic values model, for instance, varying rewards in crowdsourcing tasks or varying urgency levels in damage control tasks. We discuss this novel problem, prove the benefit of time-dependence empirically and present an efficient approximative solution, optimized for fast response systems. Our approach is the first time-dependent variant of the AOP to be evaluated on a large scale, fine-grained, real-world road network. We show that optimal solutions are infeasible and solutions to the static problem are often invalid. We propose an approximate dynamic programming solution which produces valid paths and is orders of magnitude faster than any optimal solution.
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