Ilarri Sergio, Trillo-Lado Raquel, Marrodán Lorena
I3A, University of Zaragoza, Zaragoza, Spain.
SN Comput Sci. 2022;3(4):281. doi: 10.1007/s42979-022-01105-0. Epub 2022 May 7.
Air pollution due to the presence of small particles and gases in the atmosphere is a major cause of health problems. In urban areas, where most of the population is concentrated, traffic is a major source of air pollutants (such as nitrogen oxides or and carbon monoxide or CO). Therefore, for smart cities, carrying out an adequate traffic monitoring is a key issue, since it can help citizens to make better decisions and public administrations to define appropriate policies. Thus, citizens could use these data to make appropriate mobility decisions. In the same way, a city council can exploit the collected data for traffic management and for the establishment of suitable traffic policies throughout the city, such as restricting the traffic flow in certain areas. For this purpose, a suitable modelling approach that provides the estimated/predicted values of pollutants at each location is needed. In this paper, an approach followed to model traffic flow and air pollution dispersion in the city of Zaragoza (Spain) is described. Our goal is to estimate the air quality in different areas of the city, to raise awareness and help citizens to make better decisions; for this purpose, traffic data play an important role. In more detail, the proposal presented includes a traffic modelling approach to estimate and predict the amount of traffic at each road segment and hour, by combining historical measurements of real traffic of vehicles and the use of the SUMO traffic simulator on real city roadmaps, along with the application of a trajectory generation strategy that complements the functionalities of SUMO (for example, SUMO's calibrators). Furthermore, a pollution modelling approach is also provided, to estimate the impact of traffic flows in terms of pollutants in the atmosphere: an R package called is used to estimate the amount of generated by the traffic flows by taking into account the vehicular fleet composition (i.e., the types of vehicles, their size and the type of fuel they use) of the studied area. Finally, considering this estimation of , a service capable of offering maps with the prediction of the dispersion of these atmospheric pollutants in the air has been established, which uses the and takes into account the meteorological conditions and morphology of the city. The results obtained in the experimental evaluation of the proposal indicate a good accuracy in the modelling of traffic flows, whereas the comparison of the prediction of air pollutants with real measurements shows a general underestimation, due to some limitations of the input data considered. In any case, the results indicate that this first approach can be used for forecasting the air pollution within the city.
大气中存在的小颗粒和气体导致的空气污染是健康问题的主要原因。在大多数人口集中的城市地区，交通是空气污染物（如氮氧化物或 和一氧化碳或CO）的主要来源。因此，对于智慧城市而言，进行充分的交通监测是一个关键问题，因为它可以帮助市民做出更好的决策，并帮助公共管理部门制定适当的政策。这样，市民可以利用这些数据做出适当的出行决策。同样，市议会可以利用收集到的数据进行交通管理，并在全市范围内制定合适的交通政策，例如限制某些区域的交通流量。为此，需要一种合适的建模方法来提供每个位置污染物的估计/预测值。本文描述了在西班牙萨拉戈萨市对交通流量和空气污染扩散进行建模所采用的方法。我们的目标是估计城市不同区域的空气质量，提高公众意识并帮助市民做出更好的决策；为此，交通数据起着重要作用。更详细地说，提出的方案包括一种交通建模方法，通过结合车辆实际交通的历史测量数据以及在真实城市路线图上使用SUMO交通模拟器，以及应用补充SUMO功能的轨迹生成策略（例如，SUMO的校准器），来估计和预测每个路段和每个小时的交通流量。此外，还提供了一种污染建模方法，以估计交通流量对大气中污染物的影响：使用一个名为 的R包，通过考虑研究区域的车辆车队组成（即车辆类型、尺寸和使用的燃料类型）来估计交通流量产生的 量。最后，考虑到对 的这种估计，已经建立了一种能够提供预测这些大气污染物在空气中扩散情况的地图的服务，该服务使用 并考虑了城市的气象条件和形态。该方案的实验评估结果表明，在交通流量建模方面具有良好的准确性，而将空气污染物预测结果与实际测量结果进行比较时，由于所考虑的输入数据存在一些局限性，总体上显示出低估的情况。无论如何，结果表明这种初步方法可用于预测城市内的空气污染。
