DeepAI AI Chat
Log In Sign Up

Greenhouse Gas Emission Prediction on Road Network using Deep Sequence Learning

04/16/2020
by   Lama Alfaseeh, et al.
Ryerson University
0

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

READ FULL TEXT
01/06/2023

Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads

Accurate traffic volume and speed prediction have a wide range of applic...
11/01/2019

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

Road surface friction significantly impacts traffic safety and mobility....
12/12/2019

DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks

Accurate real-time forecasting of key performance indicators (KPIs) is a...
04/21/2020

ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

We investigate the problem of predicting driver behavior in parking lots...
01/24/2020

DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction

Over the past decade, several approaches have been introduced for short-...