Zahid, Tarek (UNLV), Morris, Brendan (University of Nevada, Las Vegas)

Using Deep Traffic Prediction for EMFAC Emission Estimation and Visualization

Scheduled for presentation during the Regular Session "Traffic prediction and estimation IV" (ThBT7), Thursday, September 26, 2024, 14:50−15:10, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

Accurate traffic emission prediction is crucial for developing effective traffic management strategies and reducing the environmental impact of transportation. In this study, we propose an approach to traffic emission prediction for Las Vegas using a graph-based transformer model and an environmental factors simulator (EMFAC) model. The transformer model is employed to predict traffic speed and flow and the predicted values are then used as input parameters to the EMFAC model to estimate traffic emissions. To address the lack of EMFAC model parameters for Las Vegas, we utilize the traffic profile data from San Bernardino, California, which exhibits similar traffic characteristics to Las Vegas. The proposed method is evaluated using real-world traffic data and demonstrates promising results in terms of accuracy and effectiveness.