Evaluating crisis perturbations on urban mobility using adaptive reinforcement learning
Evaluating crisis perturbations on urban mobility using adaptive reinforcement learning
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DOI：
10.1016/j.scs.2021.103367
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发表时间：
2021-09-22
影响因子：
11.7
通讯作者：
Mostafavi, Ali
中科院分区：
文献类型：
--
作者：
Fan, Chao;Jiang, Xiangqi;Mostafavi, Ali
本研究的目的是提出并测试一种自适应强化学习模型，该模型能够在正常情况下学习人类的流动性模式，并模拟洪水、野火和飓风等危机引起的扰动时的流动性。了解和评估人员流动模式，如目的地和轨迹选择，可以为紧急情况下因中断而出现的拥堵和道路封闭提供信息。与人类运动轨迹有关的数据很少，特别是在紧急情况下，这限制了从经验数据中学习的现有城市流动模型的应用。需要能够从正常情况下产生的数据中学习流动模式并能够适应紧急情况的模型，以便为紧急反应和城市复原力评估提供信息。为了弥补这一差距，本研究创建并测试了一个自适应强化学习模型，该模型可以预测运动的目的地，估计每个起点和目的地对的轨迹，并检查扰动对人类与目的地和运动轨迹相关的决策的影响。利用INRIX的数百万轨迹数据，在休斯顿和2017年8月哈维飓风造成的洪水情景中展示了所提出的模型的应用。实验结果表明，在模型学习阶段，该模型可以达到76%以上的准确率和召回率。预测轨迹中行程的平均百分比误差为4.29%，与经验数据中行程的平均百分比误差为4.29%。此外，预测网格单元内的车辆密度与洪水期间路段上的交通速度和网格单元内的淹没强度呈负相关。仿真结果进一步表明，该模型能够预测城市洪涝灾害引起的交通模式和拥堵情况。分析结果表明，该模型具有分析危机期间城市流动性的能力，可以为公众和决策者提供关于减少危机对城市流动性影响的应对策略和复原力规划的信息。
The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and evaluating human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. Employing millions of trajectory data from INRIX, the application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76% precision and recall at the model learning stage. The mean percentage error of the travel distance in predicted trajectories is 4.29%, compared to the travel distances in empirical data. In addition, predicted density of vehicles in grid cells are negatively associated with the traffic speed on road segments and inundation intensity in grid cells during the flooding. The results from the simulation further show that the model could predict traffic patterns and congestion resulting from urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility.