Document Server@UHasselt >
Education >
School for Transportation Sciences >
Master theses >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/17426
|
Title: | A decision support system to reducing CO2 and black carbon emissions by adaptive traffic management |
Authors: | Hosseinzadeh Bahreini, Samaneh |
Advisors: | JANSSENS, Davy |
Issue Date: | 2014 |
Publisher: | UHasselt |
Abstract: | Transport sector is one of the main sources of environmental pollution and traffic
congestion in urban areas. The urban air pollution affects not only human health but also
ecosystem through global warming. Black carbon (BC), a product of incomplete combustion
of fossil fuels, is considered as one of the main contributors of global warming. Although a
lot of research has been done to investigate transport-related pollutions role on public health
and air quality, further improvement and innovative ways such as applying intelligent
transport systems should be developed to reduce these effects.
CARBOTRAF is an European project to develop a traffic management system to
implement Intelligent Traffic Systems based upon measurements and modeling of traffic,
CO2 emissions, black carbon emission and local air quality. This master thesis as a part of
evaluation of CARBOTRAF project, concentrated on data analysis in order to achieve three
different objectives. An extensive literature review is presented in this respect and to do more
practical analysis, the measured BC concentration and traffic parameters data (total flow,
speed, acceleration, number of heavy vehicles and number of passenger cars) from two host
cities of Glasgow, Scotland and Graz, Austria which were chosen as test sites in
CARBOTRAF project, was investigated.
As the first objective which is done by available data from Graz, the relation between
roadside BC concentrations measured by three detectors; Graz- Nord as urban background of
BC concentration, |
Notes: | master in de mobiliteitswetenschappen-mobiliteitsmanagement |
URI: | http://hdl.handle.net/1942/17426 |
Category: | T2 |
Type: | Theses and Dissertations |
Appears in Collections: | Master theses
|
Files in This Item:
|
Description |
Size | Format |
 | N/A | 10.07 MB | Adobe PDF |
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|