Data underlying the publication: Virus spreading in ride-pooling networks. Can ride-pooling become a safe and sustainable mobility alternative for pandemic urban systems?

The dataset contains results of experiments for the study of our research https://arxiv.org/abs/2011.12770. Data is used to generate results with this reproducible notebook: https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynb<br>Files:- png with maps- .csv with epidemic modelling results (For any given day, the model outputs information about the number of travellers in each state (S-I-Q-R) and newly infected travellers, based on which we can reproduce epidemic spreading profiles.)<br>Abstract of the study:Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

本数据集包含本项研究的实验结果，该研究的论文链接为：https://arxiv.org/abs/2011.12770。相关实验结果可通过以下可复现的Jupyter Notebook生成：https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynb

数据集包含两类文件：
1. 地图可视化文件（PNG格式）；
2. 疫情建模结果文件（CSV格式）：针对任意给定日期，该模型将输出各区域内处于易感（S）、感染（I）、隔离（Q）、康复（R）状态的旅行者数量，以及新增感染旅行者人数，基于上述数据可复现疫情传播态势。

本项研究的摘要如下：
城市出行亟需可持续替代出行方案，以维持COVID-19大流行背景下城市的正常运转。合乘出行（即多名旅行者共享同一辆车辆）不仅对出行平台及其用户具有吸引力，亦有助于提升城市出行系统的可持续性。然而，在COVID-19大流行的背景下，考虑到个人与公共健康风险，合乘出行作为安全高效的替代出行方式的潜力至今尚未明确。
为解答这一问题，本研究结合流行病学与行为学共享性模型，以阿姆斯特丹为应用场景，探究合乘出行旅行者间的病毒传播风险。研究结果初看令人担忧：仅需少量初始感染旅行者，即可将病毒传播至数百名合乘用户。若无干预措施，合乘出行系统或大幅加剧病毒传播态势。尽管如此，本研究仍找到了一项有效的管控手段：可在疫情暴发前阻断传播（将暴发阈值从800例感染降至50例），且不会牺牲合乘出行的运营效率。为同行旅行者固定匹配行程，可切断原本高度连通的接触网络，将病毒局限于小型社区内，从而阻止疫情暴发。