标题： 常见场景下行人动力学模型的基准测试：基于力的模型评估 显示英文标题

标题： Benchmarking Pedestrian Dynamics Models for Common Scenarios: An Evaluation of Force-Based Models

摘要： 在行人动力学方面的大量研究主要集中在拥挤条件及其相关现象上，例如车道形成、疏散等。 已经开发了几种基于力的模型来预测这些情况下的行为。 相比之下，在中等至低密度情况下的研究存在显著的空白。 这些场景在全世界都非常常见，包括像印度这样的高人口国家。 此外，基于力的模型的细节在这些密度下预计会产生显著影响，而拥挤、几乎被填满的条件可能主要由接触力支配。 在本研究中，我们解决了这一空白，并全面评估了不同基于力的模型在一些常见场景中的性能。 为此，我们在四种情况下进行了受控实验：避开静止障碍物、相向而行时的位置交换、超车以达到共同目标以及穿过障碍物迷宫。 性能评估包括两个阶段和六个评估参数——成功轨迹、重叠比例、振荡强度、路径平滑度、速度偏差和旅行时间。 首先，模型必须满足至少80%成功轨迹的资格标准，其次，根据从实验数据中建立的截止值对模型进行评分。 我们评估了五个基于力的模型，其中最好的模型得分为57.14%。 因此，我们的研究结果揭示了这些模型在这些常见情况下产生行人动力学准确预测能力的重大不足。 显示英文摘要

摘要： Extensive research in pedestrian dynamics has primarily focused on crowded conditions and associated phenomena, such as lane formation, evacuation, etc. Several force-based models have been developed to predict the behavior in these situations. In contrast, there is a notable gap in terms of investigations of the moderate-to-low density situations. These scenarios are extremely commonplace across the world, including the highly populated nations like India. Additionally, the details of force-based models are expected to show significant effects at these densities, whereas the crowded, nearly packed, conditions may be expected to be governed largely by contact forces. In this study, we address this gap and comprehensively evaluate the performance of different force-based models in some common scenarios. Towards this, we perform controlled experiments in four situations: avoiding a stationary obstacle, position-swapping by walking toward each other, overtaking to reach a common goal, and navigating through a maze of obstacles. The performance evaluation consists of two stages and six evaluating parameters - successful trajectories, overlapping proportion, oscillation strength, path smoothness, speed deviation, and travel time. Firstly, models must meet an eligibility criterion of at least 80\% successful trajectories and secondly, the models are scored based on the cutoff values established from the experimental data. We evaluated five force-based models where the best one scored 57.14\%. Thus, our findings reveal significant shortcomings in the ability of these models to yield accurate predictions of pedestrian dynamics in these common situations.