Zhang Shiyuan, Hua Lianlian, Yu Bo
School of Statistics, Jilin University of Finance and Economics, Chang Chun, Ji Lin 130117, China.
School of Economics and Management, Inner Mongolia University of Technology, Hohhot, Inner Mongolia 010051, China.
Transp Res E Logist Transp Rev. 2022 May;161:102724. doi: 10.1016/j.tre.2022.102724. Epub 2022 Apr 27.
Subways play an important role in public transportation to and from work. In the traditional working system, the commuting time is often arranged at fixed time nodes, which directly leads to the gathering of "morning peak" and "evening peak" in the subway. Under the COVID-19 pandemic, this congestion is exacerbating the spread of the novel coronavirus. Several countries have resorted to the strategy of stopping production to curb the risk of the spread of the epidemic seriously affecting citizens' living needs and hindering economic operation. Therefore, orderly resumption of work and production without increasing the risk of the spread of the epidemic has become an urgent problem to be solved. To this end, we propose a mixed integer programming model that takes into account both the number of travelers and the efficiency of epidemic prevention and control. Under the condition that the working hours remain the same, it can adjust the working days and commuting time flexibly to realize orderly off-peak travel of the workers who return to work. Through independent design of travel time and reasonable control of the number of passengers, the model relaxes the limitation of the number of subway commuters and reduces the probability of cross-travel between different companies. We also take the data of Beijing subway operation and apply it to the solution of our model as an example. The example analysis results show that our model can realize the optimal travel scheme design of returning to work at the same time node and avoiding the risk of cross infection among enterprises under different epidemic prevention and control levels.
地铁在上下班的公共交通中发挥着重要作用。在传统的工作制度下，通勤时间往往安排在固定的时间节点，这直接导致地铁出现“早高峰”和“晚高峰”客流聚集。在新冠疫情下，这种拥堵正在加剧新型冠状病毒的传播。一些国家采取停工策略以遏制疫情传播风险，因为这严重影响公民生活需求并阻碍经济运行。因此，在不增加疫情传播风险的情况下有序复工复产已成为亟待解决的问题。为此，我们提出一个混合整数规划模型，该模型兼顾出行人数和疫情防控效率。在工作时长不变的情况下，它能够灵活调整工作日和通勤时间，以实现复工人员的错峰出行。通过自主设计出行时间并合理控制客流量，该模型放宽了地铁通勤人数的限制，降低了不同企业间交叉出行的概率。我们还以北京地铁运营数据为例，将其应用于模型求解。实例分析结果表明，我们的模型能够在同一时间节点实现复工的最优出行方案设计，并避免不同疫情防控水平下企业间交叉感染的风险。
