Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

So, Jaehyun; Motamedidehkordi, Nassim; YingJie, Wu; Busch, Fritz; Choi, Keechoo

doi:10.1080/15568318.2017.1363328

Cited by 14 publications

(4 citation statements)

References 11 publications

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“…Since the introduction of a general speed limit might significantly impact the driving patterns throughout the network, leading to increased travel times and longer lengths of stay within the network, future studies could focus on a simulation-based approach to validate the assumed distribution functions and their impact on network-wide CO 2 emission savings. Microscopic traffic simulation would also allow for the inclusion of VSL-based calculations [56][57][58], drastically improving on the applicability of our methodology to practical debates and potentially offsetting concerns on the topic of negative changes in driving patterns.…”

Section: Discussionmentioning

confidence: 99%

Speed Limit Induced CO2 Reduction on Motorways: Enhancing Discussion Transparency through Data Enrichment of Road Networks

Kunkler¹,

Braun²,

Kellner³

2021

Sustainability

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Considering climate change, recent political debates often focus on measures to reduce CO2 emissions. One key component is the reduction of emissions produced by motorized vehicles. Since the amount of emission directly correlates to the velocity of a vehicle via energy consumption factors, a general speed limit is often proposed. This article presents a methodology to combine openly available topology data of road networks from OpenStreetMap (OSM) with pay-per-use API traffic data from TomTom to evaluate such measures transparently by analyzing historical real-world circumstances. From our exemplary case study of the German motorway network, we derive that most parts of the motorway network on average do not reach their maximum allowed speed throughout the day due to traffic, construction sites and general road utilization by network participants. Nonetheless our findings prove that the introduction of a speed limit of 120 km per hour on the German autobahn would restrict 50.74% of network flow kilometers for a CO2 reduction of 7.43% compared to the unrestricted state.

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Section: Discussionmentioning

confidence: 99%

Speed Limit Induced CO2 Reduction on Motorways: Enhancing Discussion Transparency through Data Enrichment of Road Networks

Kunkler¹,

Braun²,

Kellner³

2021

Sustainability

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“…A function of acceleration and velocity observed from a dynamometer experiment has been applied to the INTEGRATION model for the estimation of emissions from measured fuel consumption. Additionally, it is further developed for the simulation and optimization of trip-based microscopic traffic [21]. Using more parameters, including 55 parameters, a model named CMEM is proposed by a group of researchers to estimate parameters for a wide range of light-duty vehicles.…”

Section: Vehicle Emissions Estimation Modelsmentioning

confidence: 99%

Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

Hien

Kor

2022

Applied Sciences

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Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.

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“…Differently, So et al [46] proposed an integrated use of AIMSUM, i.e. the traffic micro simulator, CARSIM, i.e.…”

Section: Related Workmentioning

confidence: 99%

Integrating tools for an effective testing of connected and automated vehicles technologies

Pariota

Coppola²,

Costanzo

et al. 2020

IET Intelligent Transport Systems

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Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

Cited by 14 publications

References 11 publications

Speed Limit Induced CO2 Reduction on Motorways: Enhancing Discussion Transparency through Data Enrichment of Road Networks

Speed Limit Induced CO2 Reduction on Motorways: Enhancing Discussion Transparency through Data Enrichment of Road Networks

Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

Integrating tools for an effective testing of connected and automated vehicles technologies

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