Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS).

We developed a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and GHG ER of previous time steps. In particular, various specifications of the long short-term memory (LSTM) networks with explanatory variables were examined, and were compared with clustering and the autoregressive integrated moving average (ARIMA) model with explanatory variables. The downtown Toronto road network was used as the study area, and highly detailed data were synthesized using a calibrated traffic microsimulation and MOVES.

It was found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performed the best while adopting two hidden layers, and when the hyper-parameters were systematically tuned. Adopting a 30-second updating interval slightly improved the correlation between true (simulated) and predicted GHG ERs (from predictive models), but contributed negatively to the prediction accuracy as reflected in the increased root mean square error (RMSE) value.

Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way for various applications, e.g. anticipatory eco-routing in large-scale road networks to alleviate the adverse impact on global warming.

Dr. Lama Alfaseeh earned her Bachelor’s Degree in Civil Engineering in 2006 and a Masters Degree in Construction Project Management in 2011 from Damascus University. She started her PhD in 2016, joined the Laboratory of Innovations in Transportation (LiTrans) at Ryerson University in 2017, and defended her dissertation in 2020. Lama was supervised by Dr. Bilal Farooq and her research investigated the impact of employing intelligent vehicles in a distributed routing environment. Lama utilized intelligent transportation systems (ITS) to mitigate the undesired effect on the environment and health. She started a postdoc position in November 2020 where she has been developing predictive models for climatic variables to help structural engineers proactively design concrete structures while considering the impact of climate change.

Presented by University of Toronto ITE Student Chapter, UT-ITE. All are welcome.

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