Reassessing the impact of eco-driving guidance considering the heterogeneity of driver acceptance

Eco-driving can reduce energy consumption, yet existing assessments often overestimate its effectiveness by neglecting driver acceptance heterogeneity. This study presents a simulation-based modeling framework that evaluates an eco-driving algorithm at signalized intersections, incorporating acceptance probabilities through two complementary scenarios: a non-connection, purely human-driving environment, and a vehicle-to-vehicle (V2V) communication framework. A Monte Carlo simulation is used to assess energy consumption and travel time under varying market penetration rates (MPRs). Results reveal a notable divergence from full compliance assumptions, with energy savings overestimated, especially when MPR is below 0.5 without V2V communication. Deceleration guidance yields greater energy savings than acceleration guidance, supporting phase-specific deployment strategies. Additionally, results from V2V communication-based simulation still depend on drivers’ willingness to accept guidance, highlighting the need for adaptive strategies. This study contributes to transport dynamics research by integrating probabilistic behavioral responses into eco-driving assessment, revealing how micro-level heterogeneity propagates into fleet-level system effects.