## 2 Previous Works
The EPR model involves two stages: a genetic algorithm is used in the first stage based on the numerical regression to search for symbolic structures, whereas in the second stage, symbolic structure parameters are determined based on the linear least squares techniques. Their results showed that the ANN model is more reliable than the EPR model. The highest value of the correlation coefficient was measured at 0.85 based on NN and 0.41 using EPR. This study indicated that NN modeling can be efficiently utilized for air quality forecasting. On the other hand, ref. [25] developed a model based on the NN algorithm and data obtained from field survey to estimate the hourly traffic emissions near roads. The authors used different parameters such as traffic data, meteorology, proximity to roads and road direction. This model is considered as an efficient approach for predicting pollutant near a road. Although they used geographic information as a parameter, their results did not contain spatial prediction results such as maps. They only presented a statistical analysis. Results showed that the highest correlation coefficient for the CO prediction was 0.879. Ref. [26] conducted the most relevant studies that combined the NN model and the spatial prediction model. They presented an approach that combined the linear-chain conditional random field algorithm and ANN model to generate real-time air pollution maps. They utilized the data recorded from monitoring stations and the traffic data collected from the field while geographic parameters like land use and road network were derived using GIS data. Their results showed the air pollutants prediction on maps with (\(1\times 1\)) km spatial resolution. However, the developed model did not consider many issues like uncertainty, modeling multifactor and nonlinearity. Although several authors have attempted to overcome these issues, they principally focused on the integration of big data and the large scale modeling. On the other hand, most of these models deal with large quantities of data, expensive equipment, and complex data processing models, which require substantial time, cost and other resources. In this paper, we presented a hybrid model to produce microscale prediction maps considering toll gate locations, as well as the other parameters listed in the literature. The model is developed by combining the metaheuristic optimization technique and ANN algorithm to predict traffic emissions based on a small number of training data and avoiding transferability issues. The metaheuristic optimization algorithms like correlation-based feature selection models which have the ability to find best model's predictors in a short time were compared to other optimization techniques. Also, ANN algorithms are suitable for prediction based on few training data. The major contributions of this work lie in producing highly accurate predictive maps and providing a description of the high variation of traffic emissions on roads and tollgate areas. Other significant advantages may include easy implementation of the proposed model in open source GIS software where the non-expert users can utilize the model for rapid simulations and assessments of vehicular emissions based on microscale prediction maps. Also, the users can design GIS models based on their needs. We proposed that the combination of metaheuristic optimization and machine learning algorithms could help improving the forecasting of CO emissions on roads, highways and in tollgate areas.