  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 develop 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 the GHG ER of previous
time steps. In particular, various specifications of the long-short term memory
(LSTM) networks with exogenous variables are examined and compared with
clustering and the autoregressive integrated moving average (ARIMA) model with
exogenous variables. The downtown Toronto road network is used as the case
study and highly detailed data are synthesized using a calibrated traffic
microsimulation and MOVES. It is found that LSTM specification with speed,
density, GHG ER, and in-links speed from three previous minutes performs the
best while adopting 2 hidden layers and when the hyper-parameters are
systematically tuned. Adopting a 30 second updating interval improves slightly
the correlation between true and predicted GHG ERs, but contributes negatively
to the prediction accuracy as reflected on the increased root mean square error
(RMSE) value. Efficiently predicting GHG emissions at a higher frequency with
lower data requirements will pave the way to non-myopic eco-routing on
large-scale road networks {to alleviate the adverse impact on the global
warming
