Intelligent Transportation Systems-Enabled Optimal Emission Pricing Models for Reducing Carbon Footprints in a Bimodal Network

Abstract

Scientists and policymakers intend worldwide emissions reduction of up to 80% of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) in the next four decades to stabilize atmospheric concentrations. Henceforth, an immediate response from the transportation sector, one of the largest producers of GHGs (up to 30% in the United States), is critical for GHGs reduction. 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. 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 bilevel 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 article is generalizable and can be applied to any transportation network.

Is Smart Growth Associated with Reductions in Carbon Dioxide Emissions?

“…One benefit of simulations is that link-level vehicular flows can be extracted to calculate emissions, providing the details needed to accurately estimate emissions (e.g., speed, acceleration, and congestion). Also, they can be used to anticipate future scenarios with innovative technology adoption such as eco-routing systems (14,15), dynamic ecodriving technology (5), and intelligent transportation systems (16) based on optimal emissions pricing (17). However, numerous assumptions must be made regarding future travel behavior, VMT, vehicles, fuel use, regional distribution of vehicle age, and the market penetration of information technology.…”

Joint optimization of bimodal transit networks in a heterogeneous environment considering vehicle emissions

“…There are some studies relating the emission impact assessment to public transit policy (e.g., Banister and Button, 1993;Tayarani et al, 2018;Yang et al, 2020). Considering users' responses to a planner's policies, Sharma and Mishra (2013) introduced a bi-level optimization approach to devise an optimal emission pricing mechanism. Besides, Li et al (2018) developed a cost-benefit approach where the emission cost was considered to solve a mixed bus fleet management problem.…”

Intelligent Transportation Systems☆

“…Travelers can review current travel conditions including congestion, incidents, and parking availability, as well as bus or rail schedules and routes when planning their trip. Sharma and Mishra (2013) considered ITS for dynamic emissions pricing and showed the potential to reduce network fuel consumption and congestion through modal shifts to public transit.…”