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Variations in carbon emissions from vehicles at signalised intersections

Variations in carbon emissions from vehicles at signalised intersections
Variations in carbon emissions from vehicles at signalised intersections
Carbon emissions from road transport make up 20% of the total greenhouse gas emissions in the UK. Therefore, reducing carbon emissions from road transport is significant in reaching carbon reduction targets. In urban areas where signal controlled intersections are common, carbon emissions from vehicular traffic can be aggravated by aggressive driving and interruptions induced by traffic control. Considerable variations in speed and acceleration profiles could be observed between high carbon and low carbon driving. In view of the immediate effects that changing driving behaviour could have on carbon emissions without extra cost, this study had investigated the variations in carbon emissions at signalised intersection, which includes the scale of impacts of changing driving behaviour and flow interruption on carbon emissions. Characteristics which lead to high CO2 emissions could then be modified by addressing the behavioural change and control strategies. High frequency real world driving data was collected using the TRG highly instrumented vehicle. The vehicle was equipped with a number of on-board systems, i.e., on-board emission measurement system, velocity box, on-board diagnostic unit, Dashdyno and video recorder. Aggressive and economical driving styles observed for two drivers during initial tests showed distinct differences in terms of speed profiles and fuel consumption. These initial tests were used to examine the nature and scale of potential impacts on fuel consumption and to design main field tests. Natural driving observed from twenty nine drivers from the main field tests also showed significantly different levels of carbon emissions at signalised intersections, which were caused by variations in both driving behaviour and traffic control. In terms of driving behaviour, changing the worst driving to the best driving during interrupted driving was found to reduce CO2 emissions significantly. The carbon reductions were collectively contributed by 1) applying soft acceleration and keeping acceleration below 0.6m/s2 during the acceleration mode and 2) reducing leaving speed at intersections, 3) practising smooth deceleration and stable speed during the deceleration mode and 4) applying the idle-stop system. Carbon emission rates of different vehicles may vary from one to another. However, it was found that the amount of carbon savings demonstrated in this study could be possibly achieved by other internal combustion vehicles of the same class, and by hybrid electric vehicles to a lesser extent. In this study, changing driving behaviour is recommended as a cost effective way to achieve carbon reduction.
Ing, Koh
b827b901-c946-4f2b-aa40-31b5d5962580
Ing, Koh
b827b901-c946-4f2b-aa40-31b5d5962580
Mcdonald, Michael
cd5b31ba-276b-41a5-879c-82bf6014db9f

Ing, Koh (2011) Variations in carbon emissions from vehicles at signalised intersections. University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis, 200pp.

Record type: Thesis (Doctoral)

Abstract

Carbon emissions from road transport make up 20% of the total greenhouse gas emissions in the UK. Therefore, reducing carbon emissions from road transport is significant in reaching carbon reduction targets. In urban areas where signal controlled intersections are common, carbon emissions from vehicular traffic can be aggravated by aggressive driving and interruptions induced by traffic control. Considerable variations in speed and acceleration profiles could be observed between high carbon and low carbon driving. In view of the immediate effects that changing driving behaviour could have on carbon emissions without extra cost, this study had investigated the variations in carbon emissions at signalised intersection, which includes the scale of impacts of changing driving behaviour and flow interruption on carbon emissions. Characteristics which lead to high CO2 emissions could then be modified by addressing the behavioural change and control strategies. High frequency real world driving data was collected using the TRG highly instrumented vehicle. The vehicle was equipped with a number of on-board systems, i.e., on-board emission measurement system, velocity box, on-board diagnostic unit, Dashdyno and video recorder. Aggressive and economical driving styles observed for two drivers during initial tests showed distinct differences in terms of speed profiles and fuel consumption. These initial tests were used to examine the nature and scale of potential impacts on fuel consumption and to design main field tests. Natural driving observed from twenty nine drivers from the main field tests also showed significantly different levels of carbon emissions at signalised intersections, which were caused by variations in both driving behaviour and traffic control. In terms of driving behaviour, changing the worst driving to the best driving during interrupted driving was found to reduce CO2 emissions significantly. The carbon reductions were collectively contributed by 1) applying soft acceleration and keeping acceleration below 0.6m/s2 during the acceleration mode and 2) reducing leaving speed at intersections, 3) practising smooth deceleration and stable speed during the deceleration mode and 4) applying the idle-stop system. Carbon emission rates of different vehicles may vary from one to another. However, it was found that the amount of carbon savings demonstrated in this study could be possibly achieved by other internal combustion vehicles of the same class, and by hybrid electric vehicles to a lesser extent. In this study, changing driving behaviour is recommended as a cost effective way to achieve carbon reduction.

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Published date: September 2011
Organisations: University of Southampton, Civil Maritime & Env. Eng & Sci Unit

Identifiers

Local EPrints ID: 349003
URI: http://eprints.soton.ac.uk/id/eprint/349003
PURE UUID: 6f72315a-e3af-4605-a0d2-4b2a08bdba10

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Date deposited: 01 Jul 2013 15:52
Last modified: 18 Jul 2017 04:46

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Contributors

Author: Koh Ing
Thesis advisor: Michael Mcdonald

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