Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

Abstract

Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

“…For example, Xu, Cao, and Kang (2019) interpolated insufficient remote sensing emission measurement data with a semisupervised learning approach and proposed a deep spatiotemporal learning network for regional emission prediction. Azeez, Pradhan, and Shafri (2018) also utilized remote sensing data to predict vehicle carbon monoxide emissions through support vector regression. In addition to remote sensing, other measurement methods like Portable Emission Measurement Systems have been employed to collect vehicle emission data and construct emission prediction models (Jaworski, Mądziel, and Lejda 2019).…”

Municipal Efforts to Reduce Greenhouse Gas Emissions: Evidence from U.S. Cities on the U.S.-Mexico Border

“…Methodologically, regression analysis, specifically, linear regressions, has been used in studies examining local sustainability [63,64]. As Azeez et al [65] point out, numerous studies conducting this type of research have used various statistical, regression, and artificial intelligence models to predict and simulate vehicular GHG emissions.…”

The Influence of Traffic-Related Air Pollution (TRAP) in Primary Schools and Residential Proximity to Traffic Sources on Histone H3 Level in Selected Malaysian Children

“…Children may be exposed to TRAP during the round-trip transport from their residences to schools, although the duration is short. Moreover, studies have proven high concentrations of air pollutants at pedestrian crossings, traffic intersections, and junctions [56,66].…”