Estimating the Effects of Pavement Condition on Vehicle Operating Costs

Chatti, Karim; Zaabar, Imen

doi:10.17226/22808

Cited by 138 publications

(161 citation statements)

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“…This needs further investigation and a comparison with findings of previous studies (e.g. [4]) is recommended.…”

Section: Discussionmentioning

confidence: 86%

“…[3]) is chosen, however, some studies (e.g. [4]) showed that the road geometry and the condition of the road infrastructure can significantly affect fuel economy.…”

Section: Introductionmentioning

confidence: 99%

“…gradient, roughness and macrotexture) (e.g. [4], [5], [6], [7]), however, most of these models base their estimates on standard drive cycles, are calibrated only for specific vehicles, or offer just a simplified mechanistic model. Although these capture the physical processes controlling the fuel consumption, they may be representative of only a few specific cases and may not describe what happens in reality.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for fuel consumption modelling of trucks

Perrotta

Parry

Neves

2017

2017 IEEE International Conference on Big Data (Big Data)

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-This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R2, and lower error.

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“…This needs further investigation and a comparison with findings of previous studies (e.g. [4]) is recommended.…”

Section: Discussionmentioning

confidence: 86%

“…[3]) is chosen, however, some studies (e.g. [4]) showed that the road geometry and the condition of the road infrastructure can significantly affect fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for fuel consumption modelling of trucks

Perrotta

Parry

Neves

2017

2017 IEEE International Conference on Big Data (Big Data)

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“…However, many more factors can be included and their trade-offs considered in order to make the pavement type selection process more sustainable. Fuel consumption, vehicle operating cost (VOC), discharge levels and time frame also differ for different pavements [97]. Diverse pavement types require different types of maintenance models and original or recycled materials, which will affect the estimate and review of sustainability [98].…”

Section: Project Levelmentioning

confidence: 99%

“…For this purpose, Monte Carlo's simulation model was employed to make histograms of energy consumption for different stages of maintenance and rehabilitation along the pavement. To assess extra fuel consumption due to rough pavement structure, the methods introduced by Chatti and Zaabar [97] were used at the network level. While these methods have multiple possible outcomes, the greatest benefit is derived from the risk assessment involved in the process of pavement management.…”

Section: Network Levelmentioning

confidence: 99%

Sustainable Development Factors in Pavement Life-Cycle: Highway/Airport Review

et al. 2016

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Sustainability has gained as much importance as management in business. Sustainable pavement development as a business practice should involve making evaluations according to the triple bottom line in the pavement life-cycle. Despite the current approaches to evaluating the social as well as economic and environmental feasibility of pavement projects (involving highway and airport infrastructure), there has recently been a lack of consensus on a methodology to guarantee sustainability upon assessment and analysis during the pavement life-cycle. As sustainability is a complex issue, this study intends to further explore sustainability and elaborate on its meaning. The second step involves a general depiction of the major sustainability appraisal tools, namely cost-benefit analysis, life-cycle cost analysis, life-cycle assessment, multi-criteria decision-making, environmental impact assessment and social life-cycle assessment, and an explanation of their cons and pros. Subsequently, the article addresses the application of an organized methodology to highlight the main factors or concepts that should be applied in sustainable pavement development and, more specifically, in sustainable pavement management. In the final step, research recommendations toward sustainability are given. This study is aimed to assist decision-makers in pavement management to plan sustainability frameworks in accordance with probable boundaries and restrictions.

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