From: 2025-09-17 14:00 to 15:00
Place: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH
Contact: emma [dot] tegling [at] control [dot] lth [dot] se

Date & Time: September 17th, 14:00-15:00
Location: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH
Speaker:  Umar Niazi
Title: Eco-driving Incentive Mechanisms for Mitigating Urban Emissions 
Abstract:  
The transportation sector is a primary contributor to global greenhouse gas emissions, making sustainable practices like eco-driving - which can reduce vehicle emissions by 10% to 45% - critically important. However, the potential for longer travel times often discourages drivers from adopting these behaviors. This presentation introduces a framework for designing incentive mechanisms that encourage eco-driving in urban networks, addressing the challenges of driver interaction and strategic behavior.

We model a system where a Transportation System Operator (TSO) provides drivers with personalized eco-driving guidance and financial incentives. Drivers' decisions are framed as a game where they choose an eco-driving level to optimize a personal trade-off between travel time and emissions, influenced by the actions of other drivers on shared routes.

The core of this talk focuses on two distinct incentive mechanisms:

1.     The First-Best Mechanism: This mechanism is designed for an ideal scenario where drivers truthfully reporttheir preferences (e.g., trip urgency vs. willingness to eco-drive). We demonstrate that when the TSO's recommendations form a Nash equilibrium, drivers are naturally motivated to comply, a condition we term "obedience".

2.     The Second-Best Mechanism: Recognizing that drivers may strategically misreport their preferences to maximize their rewards, this mechanism is designed to be "incentive-compatible". It ensures that truthful reporting is the most beneficial strategy for every driver, thereby guaranteeing compliance even under strategic behavior.

Through analysis and numerical simulations, we will compare the effectiveness of both mechanisms. While the first-best mechanism is theoretically optimal, its vulnerability to strategic manipulation can lead to higher overall emissions in practice. The second-best mechanism, though more conservative, provides a robust solution by ensuring all resulting driver behaviors are at least as eco-friendly as recommended. This work offers a practical framework for policymakers to design effective, data-driven policies that align individual driver incentives with collective environmental goals.

Bio: Umar Niazi is a Marie Curie Postdoctoral Fellow at the KTH Royal Institute of Technology. He previously conducted research as a Marie Curie Fellow at the Laboratory for Information and Decision Systems (LIDS) at MIT. He received his Ph.D. in Automatic Control Engineering from Université Grenoble-Alpes, France; M.S. in Electrical and Electronics Engineering from Bilkent University, Turkey; and B.S. in Electrical Engineering from COMSATS, Pakistan. His research interests lie at the intersection of control theory and game theory, with applications in intelligent transportation and large-scale systems.