# Ensemble Learning Traffic Model for Sofia: A Case Study
## 5 Discussion  
The present piece of research is a part of a broader research project focused on urban development scenarios, mobility, air quality and health, particularly on the path to more integrated modeling methodology development and in the context of digital transition priority themes in the European Union, Bulgaria and Sofia municipality [25; 26; 27]. Our effort was preceded by other modeling experiments [20; 28], the latter being used for air pollution dispersion modeling and simulations [29]. They illustrate the valuable aid of various machine learning methods in revealing traffic patterns based on quantitative and qualitative  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Column 1** & **Content** & **Missing Data** & **Best Model** & **Accuracy** & \(\mathbf{r^{2}}\) \\ \hline ACTUSEmean & active users of functions & 0.21\% & RF & 97.36\% & 0.97 \\ ACTLIVmean & density of inhabitants & 0.59\% & RF & 97.88\% & 0.98 \\ API16 & road traffic & 98.62\% & RF & 93.57\% & 0.90 \\ DUAT180416 & street traffic & 92.51\% & WE\_L2 & 88.83\% & 0.69 \\ NM17\_1h & hourly noise & 99.82\% & WE\_L2 & 84.10\% & 0.91 \\ \multicolumn{2}{c}{map traffic} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ NO2 & NO2 levels & 99.85\% & WE\_L2 & 91.90\% & 0.80 \\ API18 & road traffic & 99.93\% & XGB/RSO & 90.92\% & 0.61 \\ DUAT18 & street traffic & 81.25\% & WE\_L2 & 77.78\% & 0.24 \\ SO2 CC & street traffic & 99.79\% & WE\_L2 & 69.12\% & 0.67 \\ DUAT18/WE\_L2 & modeled & 81.25\% & WE\_L2 & 95.06\% & 0.65 \\ SO22 CC/WE\_L2 & modeled & 81.16\% & WE\_L2 & 94.22\% & 0.92 \\ \hline \hline \end{tabular}  
* The last two rows refer to already modeled data (Table 2), while ‘SO2 CC’ includes also some small streets.  
\end{table}
Table 4: Features with missing values predicted for the whole city network (see [19; 20; 21; 22; 23; 24] for raw data).  
Figure 5: Scatter plot of the average hourly traffic for different years predicted with AutoGluon.  
data. The main question we pose is \"What are the optimal ML algorithms for urban and peri-urban traffic modeling using various indirect features, some including incomplete and/or low resolution data?\". The obvious application is a commonly encountered setting, in which data gathering or access practices don't work smoothly enough to allow for good governance, planning and management of transportation, optimal development and healthy urban environment, leading to health issues and seriously reduced quality of life for the urban population of numerous cities. Therefore, at the EU level there are several legislative and political frameworks that support the implementation of new approaches, methods and techniques, which can benefit from the ML advantages and ML techniques can be performed in more consistent way, namely the intelligent transport systems [30], the sustainable and smart mobility [31], the current and future air quality modeling requirements [32], the access to public information [33] and the infrastructure for spatial information [34]. Most of these policies are either transposed in the legislation or officially supported at the national level of EU member states. Various national or regional agencies and local authorities adhere to the practices of decentralized management and accessibility of data. In many instances, however, expert and institutional environments are not built on mutual trust but on opportunistic distribution of influence leading to separate campaigns of provision of public data, especially in the domain of traffic, while there are cities and whole regions with severe environmental burden caused by air pollution, plus other transport related problems, such as noise, whose abatement seems really slow [35].