Real-time marginal cost estimation for eco-routing navigation guidance: Phase 2

Summary

Transportation caused 27% of U.S. greenhouse gas emissions in 2022, so reducing emissions from transportation is important to resolving the climate crisis. In an effort to achieve this goal, Google Maps recently introduced a new default navigation mode: It suggests routes to drivers to minimize their own CO2 emissions instead of minimizing their travel time. Since Google Maps is widely used, this switch could result in correspondingly large changes in how drivers choose routes. However, the goal is to reduce total systems emissions, or system-optimal (SO) eco-routing, but Google Maps routes drivers to minimize their individual emissions, which is user-optimal (UO) eco-routing. With a large proportion of drivers trying to minimize their individual emissions, that significantly changes congestion patterns, and traffic congestion increases emissions per mile compared to less-congested traffic. In fact, the researcher's preliminary work in the Institute of Electrical and Electronics Engineers Transactions on Intelligent Transportation Systems demonstrates that UO eco-routing could have higher emissions than UO travel time routing. In other words, Google Maps' well-intentioned change to routing behavior could in fact be causing transportation emissions to become worse.

The purpose of this project is to develop real-time navigation guidance that gets closer to SO eco-routing, i.e. an improved algorithm that Google Maps or similar systems could use instead. The main challenge is predicting how the route choices of drivers affect congestion for other drivers. Since the purpose is for actual use, realistic models of traffic flow behavior need to be used, which also make computation more difficult, and the system must operate in real time to be useful. Therefore, the researcher's theoretical approach to this problem is based on marginal-cost routing, which is guaranteed to find SO eco-routes if marginal costs are estimated correctly. Due to the difficulty of calculating marginal costs, the researcher proposes a heuristic to estimate them, and simulations to validate the effectiveness of the heuristic.

In Phase One of this project, researchers performed an analytical study of costs method and implemented a routing method in dynamic traffic assignment. In Phase Two, the team is developing a new dynamic traffic assignment model; exploring numerical results in city networks; and preparing a journal manuscript and final report.