Environmental effects of driving behaviour and congestion related to passenger cars

Vlieger, Ina De; Keukeleere, D De; Kretzschmar, J.G.

doi:10.1016/s1352-2310(00)00217-x

Cited by 195 publications

(103 citation statements)

References 4 publications

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“…Emissions also vary with respect to drivers' attitude, experience, gender, physical condition, and age. Aggressive driving increases emissions compared to normal driving (De Vlieger et al, 2000). Sierra Research found that most drivers spend about 2% of total driving time in aggressive mode, which contributes about 40% of total emissions (Samuel et al, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Abou-Senna

Radwan

Westerlund

et al. 2013

Journal of the Air & Waste Management Association

112

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The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO 2 ) emissions growing 40-110% over the same period. On-road vehicles are a major source of CO 2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new U.S. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NOx, PM2.5, PM10, and CO 2 . The results demonstrate that obtaining precise and comprehensive operating mode distributions on a secondby-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach.Implications: Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.

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Section: Literature Reviewmentioning

confidence: 99%

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Abou-Senna

Radwan

Westerlund

et al. 2013

Journal of the Air & Waste Management Association

112

View full text Add to dashboard Cite

show abstract

“…The behavior of drivers could be passive or aggressive, ignorant, alert, tolerant, competent, skillful and etc. According to De Vlieger [50], the amount of fuel consumption and tailpipe emission from aggressive driving increases by 20-40%, while calm driving results in a reduction of 5% compared to the normal driving. The existing driving behavior models capture drivers' tactical decisions in numerous traffic conditions.…”

Section: Systematic Literature Investigationmentioning

confidence: 99%

“…[71]. Moreover, according to De Vlieger [50], the fuel consumption rate and tailpipe emission in rural and motorway driving modes were determined to be 30-40% lower compared to the urban area, which is normally 10 L/100 km.…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

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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“…In the same way, the results show that energy consumption increases 14.6% with an aggressive driving style compared to calm driving behaviour. Literature suggests that aggressive driving results in a sharp increase in fuel consumption compared to normal driving (12-40% for an aggressive driver depending on road type and vehicle technology) (De Vlieger et al, 2000;Nuzzolo et al, 2013). The low speed profile of our route, and its consequently lower speed variability, could explain the moderate increase in energy consumption obtained in the study.…”

Section: S4mentioning

confidence: 99%

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

Travesset-Baro

Rosas-Casals

Jover

2015

Transportation Research Part D: Transport and Environment

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This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.

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Environmental effects of driving behaviour and congestion related to passenger cars

Cited by 195 publications

References 4 publications

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

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

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

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