Field Measurement and Calibration of HDM-4 Fuel Consumption Model on Interstate Highway in Florida

Jiao, Xin; Bienvenu, Michael

doi:10.1260/2046-0430.4.1.29

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…This reflects the assumption of constant speed in the experiments generally used to calibrate HDM-4 (level III adaptation) in previous studies (e.g. Zaabar and Chatti, 2010;Chatti and Zaabar, 2012;Jiao and Bienvenu, 2015).…”

Section: Real Fuel Consumptionmentioning

confidence: 71%

“…where CR1 is the rolling resistance factor specific to the tire, FCLIM is a factor depending on the driving conditions (presence of snow or water on the road surface), CR2 is the rolling resistance coefficient specific to the road surface conditions (including roughness, macrotexture, and deflection of the road), Nw is the number of wheels (tires), and b11, b12 and b13 are coefficients depending on the type and dimensions of the wheels. Further details of the model can be found in Bennett & Greenwood (2000), Bennett & Paterson (2000), Chatti & Zaabar (2012), Kerali et al (2006), Jiao & Bienvenu (2015) and Odoki et al (2013).…”

Section: The Hdm-4 Fuel Consumption Modelmentioning

confidence: 99%

See 1 more Smart Citation

Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study

Perrotta

Parry

Neves

et al. 2019

Transportation Research Part D: Transport and Environment

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Section: Real Fuel Consumptionmentioning

confidence: 71%

Section: The Hdm-4 Fuel Consumption Modelmentioning

confidence: 99%

Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study

Perrotta

Parry

Neves

et al. 2019

Transportation Research Part D: Transport and Environment

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“…Among the existing models which have been proposed to estimate fuel consumption rate, the HDM-4 fuel consumption model is one of the most widely utilized. Many studies have used this model and then carried out calibration, which is a necessary step in this methodology [5,6]. The accuracy of the HDM-4 fuel consumption model and the need for further calibration were discussed in [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

et al. 2021

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Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

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“…Although many models have been proposed to estimate fuel consumption, the HDM-4 fuel consumption model has been widely used in most cases. Many studies have used the HDM-4 fuel consumption model and then carried out calibration, which is a necessary step in this methodology [7,8]. The accuracy of the HDM-4 fuel consumption model and the need for further calibration were discussed in [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Real-world Fuel Consumption Rate of Light-duty Vehicles Based on Big Data

Iy¹,

Shi²,

Xiong³

et al. 2021

Preprint

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Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

show abstract

Field Measurement and Calibration of HDM-4 Fuel Consumption Model on Interstate Highway in Florida

Cited by 11 publications

References 3 publications

Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study

Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study

Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Estimation of Real-world Fuel Consumption Rate of Light-duty Vehicles Based on Big Data

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