Intelligent Speed Adaptation Impact of Fuel Consumption and Emission

Li, Jiuxi; Wang, Chun‐Shan; Li, Qiang; Wang, Bin

doi:10.1061/41039(345)217

Cited by 2 publications

(1 citation statement)

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“…In this paper, the general representativeness of the EMEP/EEA model is thought to require adequate discussion, specifically for the real emissive levels in scenarios with medium and high vehicular flows and consequent inconstant speed [7,8]. In fact, acceleration and deceleration imply use and dissipation of the energy provided by the engine, and directly affect fuel consumption [9][10][11] as well as the release of pollutant components [12]. Therefore, a need is observed for testing the possible limits of the currently active European official assessment models for road transport emissions.…”

Section: Introductionmentioning

confidence: 99%

Testing energy and emissions assessment models: a highway case study in virtual reality

Cristiano

2016

IET Intelligent Transport Systems

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The estimation of pollutants from road transport systems is here examined, through the comparison of emission factors calculated with static and dynamic methods. Information technology is applied to the study of human-machine, for testing currently operational assessment models in the European Union. The negligibility of the effects of variation in speed is questioned: acceleration/deceleration imply use/dissipation of energy, and directly affect energy consumption and pollutants release. An investigation based on drive simulation is conducted, proposing increasing traffic flow conditions. Two highway scenarios are simulated: an existing highway before and after major modernisation works. Benefits and detriments of its renovation are also examined. Results are processed through recent EMEP/EEA models and a commercially available system, the latter able to continuously compute the operating conditions of an engine. The correlation found between average speed and emission factors is low and not very representative. Instead, a good correlation is observed between the increase in speed variation and the increase of the emission factors. Synthetic parameters are proposed to support the analysis, based on intensity and duration of acceleration/deceleration events. In general, emission factors are substantially lower if calculated through the static models. The assumption that the effects of variation in speed can be neglected is rejected: driving cycles due to traffic flow conditions are identified as crucial for a realistic evaluation of emissions. A need is detected for the formulation of correcting parameters.

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Section: Introductionmentioning

confidence: 99%

Testing energy and emissions assessment models: a highway case study in virtual reality

Cristiano

2016

IET Intelligent Transport Systems

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Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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Intelligent Speed Adaptation Impact of Fuel Consumption and Emission

Cited by 2 publications

References 4 publications

Testing energy and emissions assessment models: a highway case study in virtual reality

Testing energy and emissions assessment models: a highway case study in virtual reality

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

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