# Ensemble Learning Traffic Model for Sofia: A Case Study
## 1 Introduction  
Transport models for urban and intercity traffic are a valuable tool for optimization and planning of mobility and accessibility, estimation of environmental pressures, sources and dispersion of chemical pollutants, such as carbon monoxide, sulfur dioxide and nitrogen oxides, inhalable particulate matter, as well as more precise evaluation of traveling time. Machine learning and deep learning algorithms provide increasingly accurate results in this field (we refer to [1; 2] for a brief review on the subject). In particular, ensemble learning algorithms [3] prove quite useful in traffic, pollution or landscape models with lots of fuzzy data. They are less prone to over-fitting than neural networks, do not require extensive preprocessing, allow for nice hyper-parameter tuning and seem to be improved against problems like multicolinearity. Our most preferred tools are Random Forest (RF) which has become the standard in this field [4], Extreme Gradient Boosting (XGB) that became famous in Kaggle competitions shortly after its release, as well as the WeightedEnsembleL2 algorithm proposed by the Python AutoML module (AutoGluon) we use for ranking, optimization and training of the models. AutoML tools are intuitive to work with and rather fast, but more importantly they provide a convenient framework for us to conduct more complex studies, e.g., considering the multi-modal setting [5] in which satellite or drone images are used along with tabular continuous, categorical and text data. Such models would have been quite difficult to implement with only the standard data science libraries, but lately they have become a standard approach in the field [6]. This study, however, relies on the classical tabular data of various sorts, as a case study, in the spiritof [7; 8]. Namely, we work with estimates of average traffic values, focusing only on spatial, not temporal distribution and use multi-feature regression to impute the missing values. Some studies rely on probabilistic methods, such as PCA [9; 10], deep learning [11] or Markov chains [12]. This article evaluates the performance of 'bagging' (RF), 'boosting' (XGB) and'stacking' (weighted ensemble) algorithms which have been studied for the purposes of both applied and fundamental research tasks related to air pollution, health and urban development performed in the context of intermediate scarcity of data, hardware, software, human and financial resources for the related degrees of complexity. There are few studies that use the ensemble learning approach for traffic related issues, more often oriented towards traffic speed [13], congestion [14] and traffic flow prediction [15], while in [7] the authors use a similar approach for a country-scale annual average daily traffic estimate. Thus, we believe our study fills a gap in the contemporary urban traffic ML modeling, especially related to Eastern European cities and other similar case studies. The case of Sofia in the context of Bulgaria and South-Eastern Europe can be described as exemplary for the low level from which the city started its environmental, green urban transition [16] and the many but still rarely well coordinated political and expert-led efforts resulting to moderate or even seemingly strong achievements [17] on the one hand, while on the other, the persistence of traffic air pollution and related problems, as well as low availability of traffic data for actual transport modeling. This includes discontinuities and many inconsistencies in data gathering, storing and management in a semi-transparent and relatively poorly communicated process among various institutions, experts and the scientific community. Our sincere hope is to see some significant improvement in the coming years, so that the ML algorithms could become even more helpful in these digitally underdeveloped cities for purposes of traffic optimization, environmental and especially air pollution screening.