Quantification of the Effect of ITS on CO2 Emissions from Road Transportation

Samaras, Zissis; Ntziachristos, Leónidas; Toffolo, Silvana; Magra, Giorgio; Garcia-Castro, Álvaro; Valdés, Cristina; Vock, Christian; Maier, Werner

doi:10.1016/j.trpro.2016.05.254

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…Previous research [1,7] reported up to 8.2% in CO 2 savings under highly congested conditions, and up to 9.5% in CO 2 savings in free-flow or low-congested conditions. These studies also reported increasing CO 2 savings as the level of "green drivers" increases, and the vehicle-km travelled decreases, due to the selection of shorter routes.…”

Section: Climate Change and Air Pollution: Co-benefits And Conflicts mentioning

confidence: 92%

“… The GN scenario assigned an impedance function defined in terms of fuel consumption, which is directly related to CO2 emissions, i.e., the assignment will seek results that minimize fuel consumption, and therefore CO2 emissions, as well. The ICT-Emissions project tested five different fuel consumption functions from different studies, and the one that best performed under congested traffic conditions was selected, i.e., the default fuel consumption function of COPERT (g/km), expressed as a function of time (g/s) [1,7]:…”

Section: Traffic Scenariosmentioning

confidence: 99%

“…The emissions reduction potential of ICT-based solutions has traditionally been viewed as secondary to impacts on traffic safety and congestion. Nevertheless, recently, different researchers have been studying the effects of these new technologies on fuel consumption and CO 2 emissions [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…The ICT-Emissions project tested five different fuel consumption functions from different studies, and the one that best performed under congested traffic conditions was selected, i.e., the default fuel consumption function of COPERT (g/km), expressed as a function of time (g/s) [1,7]:…”

mentioning

confidence: 99%

“…GN scenarios will consist of a combination of GN drivers and conventional drivers; the origin-destination (OD) matrix has been divided into two, one for GN drivers and the second for conventional drivers. For example, in the GN25 scenario, 75% of the OD matrix is assigned to conventional drivers, and the other 25% to green drivers according to Equation (1). The same reasoning applied to the SR scenarios (five penetration rates and assignment of OD matrices between the shortest route drivers and the conventional ones).…”

mentioning

confidence: 99%

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Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems

2017

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Cities worldwide suffer from serious air pollution problems and are main contributors to climate change. Green Navigation systems have a great potential to reduce fuel consumption and exhaust emissions from traffic. This research evaluates the impacts of different percentages of green drivers on traffic, CO 2 , and NO x over the entire Madrid Region. A macroscopic traffic model was combined with an enhanced macroscopic emissions model and a GIS (Geographic Information Systems) to simulate emissions on the basis of average vehicle speeds and traffic intensity at the link level. NO x emissions are evaluated, taking into account not only the exhaust emissions produced by transport activity, but also the amount of the population exposed to these air pollutants. Results show up to 10.4% CO 2 and 13.8% NO x reductions in congested traffic conditions for a 90% penetration of green drivers; however, the population's exposure to NO x increases up to 20.2%. Moreover, while traffic volumes decrease by 13.5% for the entire region, they increase by up to 16.4% downtown. Travel times also increase by 28.7%. Since green drivers tend to choose shorter routes through downtown areas, eco-routing systems are an effective tool for fighting climate change, but are ineffective to reduce air pollution in dense urban areas.

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Section: Climate Change and Air Pollution: Co-benefits And Conflicts mentioning

confidence: 92%

Section: Traffic Scenariosmentioning

confidence: 99%