Emergency situations that require the evacuation of urban areas can arise from man-made causes (e.g., terrorist attacks or industrial accidents) or natural disasters, the latter becoming more frequent due to climate change. As a result, effective and fast methods to develop evacuation plans are of great importance. In this work, we identify and propose the Bus Evacuation Orienteering Problem (BEOP), an NP-hard combinatorial optimization problem with the goal of evacuating as many people from an affected area by bus in a short, predefined amount of time. The purpose of bus-based evacuation is to reduce congestion and disorder that arises in purely car-focused evacuation scenarios. To solve the BEOP, we propose a deep reinforcement learning-based method utilizing graph learning, which, once trained, achieves fast inference speed and is able to create evacuation routes in fractions of seconds. We can bound the gap of our evacuation plans using an MILP formulation. To validate our method, we create evacuation scenarios for San Francisco using real-world road networks and travel times. We show that we achieve near-optimal solution quality and are further able to investigate how many evacuation vehicles are necessary to achieve certain bus-based evacuation quotas given a predefined evacuation time while keeping run time adequate.

翻译：城市区域应急疏散需求可能源于人为因素（如恐怖袭击或工业事故）或自然灾害，其中后者因气候变化而日益频发。因此，开发高效快速的疏散规划方法至关重要。本研究识别并提出公交疏散定向问题（BEOP），这是一个NP难组合优化问题，其目标是在预定的短时间内通过公交车从受灾区域疏散尽可能多的人员。采用公交疏散旨在缓解纯私家车疏散场景中产生的交通拥堵与混乱。为求解BEOP，我们提出一种基于深度强化学习的方法，该方法融合图学习技术，经训练后能实现快速推理，可在秒级时间内生成疏散路径。我们通过混合整数线性规划模型对疏散方案的优化间隙进行理论界定。为验证方法有效性，我们基于真实道路网络与行程时间数据构建旧金山疏散场景。实验表明，所提方法能获得接近最优的解决方案质量，并能进一步探究在预定疏散时间内，为达到特定公交疏散配额所需的最小疏散车辆数，同时保持合理的运行时间。