An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah, Akibu Mahmoud; Usmani, Raja Sher Afgun; Pillai, Thulasyammal Ramiah; Marjani, Mohsen; Hashem, Mohamed

doi:10.14569/ijacsa.2021.0120693

Cited by 14 publications

(4 citation statements)

References 48 publications

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“…This is linked to the emergence of hydrogen as a fuel and of biofuels, which can be produced from renewable fuels [210][211][212][213]. In recent years, there has been an increase in the use of, for example, artificial intelligence computing methods so that newly developed models can reflect the emissions of hybrid vehicles [214,215].…”

Section: Future Steps In Emission Modelling and Traffic Simulation An...mentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel

2023

Energies

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Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.

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Section: Future Steps In Emission Modelling and Traffic Simulation An...mentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel

2023

Energies

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“…To prepare, for example, an emissions map, modern emission models must show precisely where there are, for example, increased emissions [188]. In recent years, there has been an increase in the use of, for example, artificial intelligence computing methods so that newly developed models can reflect the emissions of hybrid vehicles [189,190].…”

Section: Future Steps In Emission Modelling and Traffic Simulation An...mentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel¹

2023

Preprint

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Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

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“…Their ndings concluded the superior performance of the LSTM model in CO 2 emissions prediction, outperforming the other techniques with the minimum Root Mean Square Error (RMSE) of 60.635. In recent times, several studies have employed the FFNS and ANFIS for prediction of CO 2 emission [20][21][22]. For instance, Mutascu et al considered a single-layer, 20neuron feed-forward ANN to forecast CO 2 trend within the United States of America (USA) [23] using FFNN modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions

Nazir,

Nagra,

Bhatti

et al. 2024

Preprint

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The forecasting of carbon dioxide (CO2) emission trends stands as a pivotal step towards achieving environmental sustainability. As countries grapple with the challenge of curbing escalating CO2 emissions, the significance of accurate forecasting has become increasingly pronounced in recent years. In this study, to unveil the trajectory of CO2 emissions in Pakistan, forecasting was done through advanced artificial intelligence (AI) driven Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. Rigorous data preprocessing techniques were applied to historical CO2 emissions data for Pakistan comprising of 76 points from year 1946 to 2021. Sequences were formulated to capture temporal dependencies, paving the way for model training and validation. The ANN, GRU, and LSTM models were meticulously designed, each bearing unique attributes for time series forecasting. The obtained results yielded valuable insights, epitomized by model evaluations and predictions. The ANN model did really well with a test MAE of 8.111, a test R² of 0.8614 and a test RMSE of 10.25. The GRU model, characterized by a test MAE of 7.936, a test R² of 0.8355 and a test RMSE of 11.25, proved its worth as well. In contrast, the LSTM model demonstrated excellence with a test MAE of 7.941, a test R² of 0.8586 and a test RMSE of 10.45. A novel ensemble approach, combining these three models, yielded a test MAE of 7.876, a test R2 of 0.869, and a test RMSE of 10.5043. Further, the models were employed to forecast CO2 emissions for Pakistan from the year 2022 to 2030. The insights gained from this study not only enhance our understanding of CO₂ emissions trends in Pakistan but also provide valuable guidance for global efforts to adopt cleaner lifestyles and sustainable choices, fostering a healthier planet for all.

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An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Cited by 14 publications

References 48 publications

Vehicle Emission Models and Traffic Simulators: A Review

Vehicle Emission Models and Traffic Simulators: A Review

Vehicle Emission Models and Traffic Simulators: A Review

Predictive analytics for environmental impact: Deep learning and AI ensemble strategies in forecasting CO2 emissions

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