UC Berkeley

Public transit is often touted as a "green" transportation option and a way for users to reduce their environmental footprint by avoiding automobile emissions. Many transit systems, however, have considerable emissions, and when vehicles run with ridership significantly below capacity, the per-passenger-kilometer emissions can be greater than for automobile. Efforts to reduce public transit emissions have centered on shifting users from more polluting modes and improving technology either by retrofitting existing vehicles or replacing them with more efficient models. I explore an approach to optimizing the design and operations of transit systems for both costs and emissions using continuum approximation models. The research identifies the Pareto frontier for designing an idealized transit network, and compares transit modes, including bus, bus rapid transit, light rail, and metro heavy rail, over four city scenarios. The slope at any point on the Pareto curve represents the cost of decreasing emissions by another unit, and this can be used to identify an emissions level that is equal to the market value of carbon. Further, I explore how the level of service for users impacts emissions: first, comparing modes at a given emissions level to see which provides the best service to users in terms of average travel time; second, incorporating travel time elasticities into the optimization to allow demand to reduce subject to increases in the travel time. Results of the parametric analysis suggest that a BRT system is a low cost and low emissions transit option for many types of cities. Choosing GHG reduction levels based on the market price of carbon has a small impact on user travel time, so further reductions may be reasonable. In general, the lowest-cost mode will provide the fastest travel time to users at a given emissions level. When shifting demand is accounted for, emissions reductions are moderated, but not eliminated, by the increase in automobile emissions when users are relatively inelastic. Including automobile emissions in the optimization shifts the problem from the agency to the city perspective and produces results that can avoid the unintended emissions consequences associated with users changing modes. This research provides a strategic framework for transit agencies to cost-effectively reduce GHG emissions, demonstrating how operational and network changes can be used to reduce the costs and emissions of a transit system. The methods can be used to estimate the system cost of GHG emissions reductions to facilitate comparison with other approaches, such as vehicle replacement or engine upgrades. One can identify the scale of reasonable reductions and estimate the net effect on emissions as service is reduced and users switch modes.