Electric vehicles and traffic related pollution reduction: a simulation model for Hamilton, Ontario, Canada

Παπαργύρη, Ευαγγελία; Papargyri, Evangelia

dc.contributor.author	Παπαργύρη, Ευαγγελία	el
dc.contributor.author	Papargyri, Evangelia	en
dc.date.accessioned	2016-09-30T08:51:02Z
dc.date.available	2016-09-30T08:51:02Z
dc.date.issued	2016-09-30
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/43699
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.10919
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική”	el
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Electric mobility	en
dc.subject	Simulation	en
dc.subject	Traffic emissions	en
dc.subject	Regression model	en
dc.subject	Hamilton CMA	en
dc.subject	Moντέλο παλινδρόμησης	el
dc.subject	Ηλεκτρικά οχήματα	el
dc.title	Electric vehicles and traffic related pollution reduction: a simulation model for Hamilton, Ontario, Canada	en
heal.type	bachelorThesis
heal.classification	Geographical information systems	en
heal.classificationURI	http://skos.um.es/unescothes/C01671
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2014-03-28
heal.abstract	This paper analyzes the potential contribution of electric vehicles in greenhouse gas (GHG) emissions reduction over the next decade. The effect of electric vehicles (EVs) on traffic related pollution is assessed at the transportation link level in the Hamilton Census Metropolitan Area (CMA) following a simulation procedure from 2006 to 2021. The traffic emissions considered in this paper are: total hydrocarbons (HC), nitrogen oxides (NOx), carbon monoxide (CO) and a basic estimate of carbon dioxide (CO2). Emissions were estimated through a number of steps. Firstly, different EV market penetration scenarios were introduced (conservative, medium, optimistic) and compared to the base case scenario where no action or minimum policy controls are supposed to take place over the next couple of decades. Scenarios were determined through a comprehensive review of penetration estimates in the literature. Following these, the spatial distribution patterns of EVs were predicted using the vehicle registration data for the Hamilton CMA along with socioeconomic data obtained from 2006 census. Different distribution patterns of EVs adoption were assessed creating sub-scenarios, in order to reflect the possible changes in the future. Subsequently, the results from the regression model were used to properly modify the Origin-Destination (OD) matrices by type of vehicle. These matrices were used as input into our traffic simulation model (TRAFFIC) that assigns traffic on the network and estimates volumes for each of the links. MOBILE 6.2C was customized to accept the new vehicle type and to compute the emission factors. The hourly emissions on each link were mapped through a geographic information system (GIS) framework after the integration of three parameters: street network, associated traffic flows and emissions (Link_emissions model). We conclude that different distribution patterns produce different spatial patterns of traffic related emissions in the links and even a modest adoption of EV technology may lead to significant reduction in traffic emissions.	en
heal.advisorName	Φώτης, Γεώργιος	el
heal.committeeMemberName	Φώτης, Γεώργιος	el
heal.committeeMemberName	Σιόλας, Άγγελος	el
heal.committeeMemberName	Βλαστός, Αθανάσιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Αγρονόμων και Τοπογράφων Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	93 σ.	el
heal.fullTextAvailability	true