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dc.contributor.authorCappiello, Alessandra
dc.contributor.authorChabini, Ismail
dc.contributor.authorLue, Alessandro
dc.contributor.authorZeid, Maya Abou
dc.contributor.authorNam, Edward K.
dc.date.accessioned2002-09-17T19:42:53Z
dc.date.available2002-09-17T19:42:53Z
dc.date.issued2002-09-17T19:42:53Z
dc.identifier.urihttp://hdl.handle.net.ezproxyberklee.flo.org/1721.1/1674
dc.description.abstractMany vehicle emission models are overly simple, such as the speed dependent models used widely, and other models are sufficiently complicated as to require excessive inputs and calculations, which can slow down computational time. We develop and implement an instantaneous statistical model of emissions (CO2, CO, HC, and NOx) and fuel consumption for light-duty vehicles, which is simplified from the physical loadbased approaches that are gaining in popularity. The model is calibrated for a set of vehicles driven on standard as well as aggressive driving cycles. The model is validated on another driving cycle in order to test its estimation capabilities. The preliminary results indicate that the model gives reasonable results compared to actual measurements as well as to results obtained with CMEM, a well-known load-based emission model. Furthermore, the results indicate that the model runs fast and is relatively simple to calibrate. The model presented can be integrated with a variety of traffic models to predict the spatial and temporal distribution of traffic emissions and assess the impact of ITS traffic management strategies on travel times, emissions, and fuel consumption.en
dc.description.sponsorshipFord Motor Company through the Ford-MIT Alliance.en
dc.format.extent590971 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectinstantaneous emissions modelingen
dc.subjectdynamic trafficen
dc.subjectvehicle emissionsen
dc.subjectfuel consumptionen
dc.subjectemission modelsen
dc.titleA Statistical Model of Vehicle Emissions and Fuel Consumptionen


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  • Ford-MIT Alliance
    Institute-wide collaboration focusing on statistical engineering, virtual education, and the environment

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