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

Perrotta, Federico; Parry, Tony; Neves, Luís; Buckland, Thomas; Benbow, Emma; Mesgarpour, Mohammad

doi:10.1016/j.trd.2018.11.001

Cited by 32 publications

(10 citation statements)

References 20 publications

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“…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]. This study was based on a limited set of tests, wherein only a small number of vehicles were tested at constant speed on selected sections under limited weather conditions.…”

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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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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“…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]. This study was based on a limited set of tests, wherein a small number of vehicles were tested at constant speed on selected sections under limited weather conditions.…”

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.

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“…This implies that both local models and default valued models are available and can be utilised in the HDM [114]. Some recent examples on the applications of HDM are Cutura et al [116] and Perrotta et al [117].…”

Section: Road Vehicle Operation Costsmentioning

confidence: 99%

Cost-Benefit Evaluation Tools on the Impacts of Transport Infrastructure Projects on Urban Form and Development

Ustaoglu¹,

Williams²

2020

Smart Urban Development

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This study reviews literature for identifying the methods in order to evaluate the impacts of key transport infrastructure provisions on urban form and peri-urban development in European Union (EU) member countries. Key impacts and linkages of transportation provision on urban development trends are identified through the international literature. These include direct impacts of transportation infrastructure provision, socioeconomic impacts, transportation network effects and energy and environmental impacts. Among the evaluation methodologies, cost-benefit analysis (CBA) is the most common approach for transport policy impact assessments both in the national project appraisal guidelines and in scientific analysis and research. Considering its extensive usage in the appraisal work, the main focus will be on the evaluation tools used within the CBA approach. The corresponding data requirements for the valuation of indicators will be also discussed in order to assess the impacts of costs and benefits of transport investments, particularly rapid rail investments, on urban form and development.

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

Cited by 32 publications

References 20 publications

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 the Records Reported by Vehicle Owners

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

Cost-Benefit Evaluation Tools on the Impacts of Transport Infrastructure Projects on Urban Form and Development

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