# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### Models and Prediction Accuracy
#### 3.3.4 Model Generalization  
To confirm the proposed approach, which consists of a traffic-based model that can be applied for any day during the morning rush hour, the accuracy of the supposed best model (3 June, 2019) was tested on the three other days. In the case of the traffic-based model, the accuracy of the classification is as follows: 64% for 30 May, 2019, 64% for 12 June, 2019, and 61% for 18 June, 2019. When this performance is compared to the models trained on a proper day, it gives the difference as follows: 2% for 30 May, 2019, 30% for 12 June, 2019, and 0% for 18 June, 2019. This result suggests that the model of 3 June, 2019 can be applied with a very high accuracy on other days, even if they are characterized by different meteorological conditions. Regarding the models based on traffic and time of the day, the performances are: 68% for 30 May, 2019, 63% for 12 June, 2019, and 58% for 18 June, 2019. The difference of accuracy in comparison to the model of the proper day is: 4% for 30 May, 2019, 10% for 12 June, 2019, and 12% for 18 June, 2019. These outcomes tend to demonstrate that the traffic-based model of 3 June, 2019 can indeed be generalizable to other days, which confirms that it is reliable to predict atmospheric pollution from a machine learning model based on the flow of vehicles in the city. Nevertheless, this generalization seems to show some limitations when the factor of time is added in the model. The fact that a model based solely on traffic is more generalizable than a one based on traffic + time of the day has a mathematical and a physical explanation. First, the machine learning approach applies the Occam's Razor principle, which states that for a similar performance, we prefer the simpler model over the more complex one ([PERSON], 2019). The more complex the model is, the higher is the probability it was fitted accidentally (overfitting). This is the reason why a dimension reduction (or regularization) should be systematically performed before applying a machine learning algorithm, in order to tackle the affliction caused by increasing the variables in a predictive model (curse of dimensionality). The second interpretation is environmental. The unstable meteorological conditions during the measurements have caused an inconsistent dilution of the pollutants over time. In consequence, the resulting models based on the time of day are less robust to predict new data. Other features, such as land use, could also be considered to improve the prediction of the background concentration of PM\({}_{2.5}\), as observed by ([PERSON], 2019). Nevertheless, this factor was discarded for the purpose of this study, because it cannot account for the dynamic of human mobility and, consequently, is limited to forecast pollution peaks.  
While we strongly insist on the good consistence between the models, a certain limitation of the study is the relatively reduced number of recorded days. However, the goal of this work goes beyond the identification of the \"hotspots\" of pollution in this specific city, in which case it would be crucial to repeatedly map the study area to assure the representability of urban pollution areas. The principal purpose of this investigation is to understand the correlation between live and dynamic traffic and urban PM pollution. We focused on a representative area of the city as it includes a rich variety of street types in the urban infrastructure. We are confident, that each section of the street has its own dynamics of traffic and pollution, which, as expected, might not be always the same. Thus, each of those few-minute records on several sections represents a separate experiment. The proposed method intends to demonstrate the potential of the real-time traffic monitoring to provide an automatic pollution mapping, as illustrated in the next section.