ITS Enabled Optimal Emission Pricing Models for Reducing Carbon Footprints in a Bi- Modal Network

Scientists and policymakers intend worldwide emissions reduction of up to 80 percent of carbon dioxide (CO2) and other greenhouse gases (GHGs) in the next four decades to stabilize atmospheric concentrations (TRB 2011). Henceforth, an immediate response from the transportation sector, one of the largest producers of GHGs (up to 30 percent in the U.S.), is critical for GHGs reduction. Possible long-term solutions towards cutting back on emissions from transportation are increasing supply-of/demand-for more energy/fuel efficient vehicles and to substantially improve the transit system (high-speed rail, passenger rail, metro) to reduce reliance on private vehicles and air travel for short trips. However, the markets are still struggling to produce supply-of/demand-for energy efficient vehicles and public debate on potential ridership of high speed rail is ongoing in the U.S. Another feasible strategy that can prove to be effective is emission pricing. Recent advancement in Intelligent Transportation Systems (ITS) offers a technical solution to implement emission pricing effectively in a reasonable period of time. Further, this strategy can foster demand for efficient vehicles and high transit ridership while reducing GHGs emission and generating revenue. As state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) struggle to find more options to reduce GHGs emission, emission pricing offers a solution. To consider emission pricing as an alternative, planners and policymakers will need tools to understand the implications on private vehicle users and the environment. Therefore, in this study, we propose models for understanding the reduction of GHGs emission and shifts of private vehicle trips to transit by implementing ITS based optimal emission pricing to reduce GHGs emission by a certain percentage in a composite transportation network (transit and highway network). The bi-level models presented in this study take into account the planner’s policy decision and the road user’s response to such policies in a simple and methodologically robust framework. The complex decision of choosing transit over private vehicle and road user behavior in the study has been studied by mode split functions and the classical user equilibrium principle. The performance of proposed models is compared to the base-case (do-nothing); reductions in total GHGs emission by optimal emission pricing shows efficacy of the models. The presented methodology in this paper is generalizable and can be applied to any transportation network.