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First published January 2007

Integrating Vehicle Emission Modeling with Activity-Based Travel Demand Modeling: Case Study of the Greater Toronto, Canada, Area

Abstract

An initial attempt is made to quantify vehicle emissions in the Greater Toronto Area (GTA) in Canada by exploiting travel information provided by activity-based 24-h models rather than conventional trip-based models. For this purpose, travel activity inputs to the Canadian version of the MOBILE6.2 model (MOBILE6.2C) are generated by relying on the travel demand modeling capabilities of the Travel Activity Scheduler for Household Agents (TASHA), a next-generation activity-based model of travel demand for the GTA. Additional input data supplied to MOBILE6.2C are obtained from Canadian sources and by running traffic assignment (using EMME/2) on the trip distribution matrix generated by TASHA. The integration of MOBILE6.2 with TASHA has provided estimates of the time of day that vehicle emissions occur. TASHA provides an explicit representation of trip starts and ends, which results in improved engine start emissions. Overall, because TASHA provides a better behavioral framework for modeling travel than conventional trip-based models, it is expected to lead to better emissions estimates. Such an effort also provides insight and experience that will be used later in the integration of TASHA with more advanced emission models, thus refining the reliability of practical tools that can be used to assess the environmental sustainability of policies.

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References

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Article first published: January 2007
Issue published: January 2007

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© 2007 National Academy of Sciences.
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Authors

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Marianne Hatzopoulou
Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario M5S 1A4, Canada.
Eric J. Miller
Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario M5S 1A4, Canada.
Bruno Santos
Department of Civil Engineering, University of Coimbra, Rua Luis Reis Santos–Polo II, 3030-290 Coimbra, Portugal.

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