Amid the escalating conflicts between urban demography, resource availability, and environmental constraints, there is an accelerating demand for emergency medical services prompted by a spectrum of factors including diseases, natural calamities, and unforeseen events. This growth is further accentuated by an imbalance in the allocation of emergency facilities, amplifying public anxiety and underscoring the urgent necessity for scientifically informed optimization of pre-hospital emergency facilities. To this end, the present study articulates specific optimization goals for facility deployment and introduces a nuanced set coverage optimization model that integrates spatiotemporal determinants of emergency service requirements. The model is fortified with a constellation of constraints, including spatial constraint, temporal constraint, and coverage constraint. For spatial constraints, we use the Monte Carlo simulation to predict the spatial distribution of emergency demands. Temporal constraints involve determining the actual travel time matrix from predicted demand points to candidate sites. Coverage constraints specify the effective demand coverage rate. This study uses Nanjing as a case example, utilizing multisource big data, including ambulance GPS logs from June 2016 to May 2017, Amap traffic congestion indicators, and survey data on existing emergency facilities in Nanjing City. By preprocessing and analyzing the existing data, this study thoroughly investigates the spatiotemporal distribution of emergency demands and the impact of traffic congestion on emergency service effectiveness. Consequently, the model incorporates constraints that ensure, under the specified planning and actual traffic conditions, 95 % of the simulated emergency demands can be met within an 8-minute on-route time. Locations for pre-hospital emergency stations are optimized using a genetic algorithm, achieving the best solution at the 120th iteration. Verification confirms 134 optimal sites; after excluding 52 existing sites, 82 potential sites are identified. The new layout plan has reduced Nanjing’s average emergency response time from 18.6 min to 12 min. Additionally, under peak and average traffic conditions, emergency demand coverage rates improved from 76.92 % and 83.18 % at 15 min to 95.61 % and 98.10 % at 12 min, respectively. These results demonstrate that the new layout significantly enhances practical application effectiveness. The approach presented in this paper addresses the previously overlooked randomness of emergency incidents and traffic conditions, offering innovative strategies for improving the efficiency and effectiveness of emergency station planning and site selection models.

中文翻译：

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南京市院前应急设施布局优化——基于多源大数据的时空分析


在城市人口、资源可用性和环境限制之间的冲突不断升级的情况下，包括疾病、自然灾害和不可预见事件在内的一系列因素促使对紧急医疗服务的需求不断增长。应急设施分配的不平衡进一步加剧了这种增长，放大了公众的焦虑，并强调了对院前应急设施进行科学优化的必要性。为此，本研究阐明了设施部署的具体优化目标，并引入了一个细致入微的集合覆盖优化模型，该模型集成了紧急服务要求的时空决定因素。该模型通过一系列约束进行强化，包括空间约束、时间约束和覆盖范围约束。对于空间约束，我们使用 Monte Carlo 模拟来预测紧急需求的空间分布。时间约束涉及确定从预测请求点到候选地点的实际行驶时间矩阵。覆盖率约束指定有效需求覆盖率。本研究以南京市为例，利用多源大数据，包括 2016 年 6 月至 2017 年 5 月的救护车 GPS 日志、高德地图交通拥堵指标以及南京市现有应急设施的调查数据。通过对现有数据进行预处理和分析，本研究深入研究了应急需求的时空分布以及交通拥堵对应急服务有效性的影响。 因此，该模型包含约束条件，确保在指定的规划和实际交通条件下，可以在 8 分钟的路线时间内满足 95% 的模拟应急需求。使用遗传算法优化院前急救站的位置，在第 120 次迭代中实现最佳解决方案。验证确认了 134 个最佳地点;在排除 52 个现有地点后，确定了 82 个潜在地点。新的布局规划将南京的平均应急响应时间从 18.6 分钟缩短到 12 分钟。此外，在高峰和平均交通条件下，应急需求覆盖率分别从 15 分钟的 76.92% 和 83.18% 提高到 12 分钟的 95.61% 和 98.10%。这些结果表明，新布局显著提高了实际应用的有效性。本文提出的方法解决了以前被忽视的紧急事件和交通状况的随机性，为提高紧急车站规划和选址模型的效率和有效性提供了创新策略。