Incentive Design for Eco-driving in Urban Transportation Networks

TL;DR

This work tackles reducing urban transportation emissions by designing budget-constrained incentives for eco-driving using a digital platform, the eco-planner, that couples a traffic microsimulator with an incentive allocator. It computes per-user feasible outcome sets $X_i$ from simulated routes and driving styles, identifies nominal outcomes $x_i^{nom}$ on each user’s Pareto front, and optimizes recommended outcomes $x_i^{rec}$ and incentives $\gamma_i$ to minimize total emissions $\xi^\top x^{rec}$ under the budget constraint $\sum_i \gamma_i \le B$. The framework compares a uniform baseline incentive to an optimal, LP-based mechanism, showing that the optimal design yields progressive increases in driver compliance and emissions reductions as budget grows, while highlighting a trade-off with travel time. The approach integrates real-time traffic simulation with economic incentives, offering a scalable method for deploying eco-driving incentives in real urban networks more efficiently than uniform schemes.

Abstract

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the `eco-planner,' a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

Figures (4)

• Figure 1: Steps in the proposed incentive mechanism (from left to right).
• Figure 2: Illustration of the road network with two alternative routes used in the simulation experiments.
• Figure 3: The feasible set computed from the feasible outcome points of both route 1 and 2. The nominal and recommended outcome points for the two driver types are indicated by circles and diamonds.
• Figure 4: Incentive mechanism under different levels of budget.