01337444

Component

This study proposes nine different models to reduce vehicular green house gas emission by designing optimal emission pricing in a given transportation system. All the models are formulated as a bi-level problem, i.e. upper level as planner’s policy variable and the lower level as road user’s response to the strategies set by the planner. The model is solved using a genetic algorithm at the upper level and a Frank Wolfe algorithm at the lower level. The developed models are tested on a small hypothetical test network and a real medium sized network of Mumbai city in India. The performance of all the proposed models is compared to the Base-Case (do nothing) and reductions in emissions shows efficacy of the models. The study makes two major contributions: first it proposes a new set of models for planners to design emission pricing for emission reduction considering possible constraints in the field, and second it realistically models both planner’s decision and user’s response to the decision to achieve minimal value of objective. Although the proposed models are solved for CO2 only, the methodology can be used for analysis of policy variables for any pollutant.

01329018

11-3248

English

21p

2011

Transportation Research Board 90th Annual Meeting

DVD

Figures (4) ; References (20) ; Tables (6)

Carbon dioxide; Environmental policy; Genetic algorithms; Greenhouse gases; Optimization; Pollutants; Pricing

Frank-Wolfe algorithm

Mumbai (India)

Environment; Highways; Planning and Forecasting; I15: Environment; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2011 Paper #11-3248

TRIS, TRB

Feb 17 2011 6:27PM