## 5 Conclusions  
Traffic emissions (e.g., traffic CO) are considered the major source of air pollution in congested urban areas, including road corridors in toll plaza areas. Traffic emission prediction models are utilized to evaluate the impacts of traffic CO emissions on the population and environment and some models are used to illustrate the spatial prediction of these emissions. In this paper, a hybrid prediction model was proposed by combining three models (CFS, MLP and GIS) to predict the traffic CO emissions and create micro-scale prediction maps in a small area at different times during the day. The final findings have shown that the proposed model scored an accuracy of 80.6% and the correlation coefficient of 0.980, RMSE of 1.2736 ppm and mean absolute error of 0.8925 ppm. We used CFS to identify and remove highly correlated parameters so that redundancy was reduced to choose the optimum parameters used in the prediction model through MLP. The simulation results showed that nine predictors were reduced to six, which contributed to an increase in the prediction accuracy. The data were collected from the field and remote sensing data (i.e., LiDAR and very high-resolution WorldView-3 satellite image), and modelling was performed in a GIS environment. In this study, we produced microscale maps for vehicular emissions. The simulated traffic CO emissions ranged from 35 ppm inside the toll plaza area to 0 ppm for areas that were located far away from the toll area. As per the microscale-prediction maps, high spatial variation in the traffic CO emissions was identified. The highest value of CO concentrations is found in traffic jam areas. Conversely, the lowest values of traffic CO emissions are distributed far from traffic activities. The highest concentrations of traffic CO were located inside the toll plaza because of the traffic jam that occurs daily in the toll areas these results give a clear indication about the relationship between the traffic activities and the traffic CO emissions. The traffic CO emissions may have a significant impact on the health of toll plaza workers, drivers and the passengers. Therefore, such a prediction model can aid decision makers to implement plans to mitigate the traffic emissions that can protect people who are working, passing through or living near toll plaza areas; this will be the main advantage of the proposed forecasting system. Traffic CO emission prediction models and GIS modeling are both efficient tools for transportation planning and traffic emission assessment. The prediction maps produced by the proposed model can be used as an effective tool in the decision-making process to identify optimum solutions which can be used to mitigate traffic jams in toll plaza areas as well as on highways and road networks. As traffic emission pollution assessment by decision makers is very complicated and usually expensive because of the high level of requirements for expert knowledge and the developed support systems, the presented traffic CO assessment model is not expensive and can easily implemented. Moreover, the traffic CO pollution concentrations vary based on the traffic condition and the number of vehicles, which requires a periodic monitoring of traffic emissions by government agencies or relevant departments. The best parameter selection analysis could be used to reduce the data collection requirements, which can lead to reduced time required, resources utilized and processing time needed. GIS modeling is a useful tool for non-expert users to implement the traffic CO impact assessments in various applications. Finally, these models can be improved by using more advanced algorithms such as deep learning algorithms and a large number of samples that can be used to increase the accuracy of the prediction process. [PERSON] conceptualized, supervised, and obtained the grant for the study. [PERSON] and O.S.A. collected and analyzed the data, performed the analyses and validation, wrote the manuscript and contributed to the re-structuring and editing of the manuscript.