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http://hdl.handle.net/11375/11685
Title: | Modeling the traffic related pollution reduction through increased use of Hybrid-Electric Vehicles (HEVs) in Hamilton, Ontario, Canada |
Authors: | Kaneda, Naoya |
Advisor: | Kanaroglou, Pavlos S. |
Department: | Geography and Earth Sciences |
Keywords: | Traffic emission;Hybrid-Electric Vehicles;Modeling;alternative fuel vehicles;Geographic Information Sciences;Human Geography;Other Geography;Geographic Information Sciences |
Publication Date: | Apr-2012 |
Abstract: | <p>In this study, the effect of HEVs on traffic related pollution was assessed in the Hamilton CMA. This thesis aimed to combine findings from these two fields in a traffic simulation procedure. By introducing the HEVs in incremental levels to the vehicle travel pattern of more than 700,000 people in the study area, changes occurring in traffic related pollution at different levels were modeled.</p> <p>The hypothetical HEV spatial distribution patterns models were derived through negative binomial regression modeling based on 2006 census data and 2008 vehicle registration data. The distribution of predetermined number of HEVs throughout the Hamilton CMA was completed through these models and results were used to modify input OD matrices for the TRAFFIC program. The link-based emissions were calculated in combination with traffic emission factors for HEV.</p> <p>The results indicated that converting 10% of the total fleet into HEVs was needed to make significant reductions to the HC and CO aggregate emissions in all five models. An important finding with the incremental HEV penetration levels was the approximately linear trend between the percent reduction in the traffic emissions and the percent of HEVs in the total fleet. This trend allows calculations of approximate traffic emission reduction expected with any HEV level. The results illustrating links with more than 10% reduction in traffic emissions indicated that HEV technology as an effective method in dealing with environmental concerns.</p> |
URI: | http://hdl.handle.net/11375/11685 |
Identifier: | opendissertations/6636 7631 2402042 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
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fulltext.pdf | 3.81 MB | Adobe PDF | View/Open |
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