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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 CO 2 eq 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 …