We live in an ever-changing world. The increasing urbanization rate has resulted, among others, in an increase in private vehicles ownership and usage. The research focuses on eco-driving and its potential to reduce greenhouse gas emissions in urban areas with high private vehicle usage. The proposed framework, called FERPS uses unsupervised machine learning techniques to categorize driving behavior into three trip-based profiles. A fuel consumption model is then employed using Gradient Boosting Decision Trees algorithm to estimate fuel consumption for upcoming trips based on dynamic driving profiles, vehicle data, and trip characteristics. FERPS is then implemented in in the inner-ring urban transport network of Athens, Greece, using the SUMO microscopic simulator. Eleven scenarios, including a BAU scenario and various FERPS penetration rates, are simulated during the morning peak hour. Emissions-related KPIs are measured for comparison and the results indicate that higher FERPS penetration rates lead to reduced emissions, highlighting the potential benefits of eco-driving in urban transport networks. By providing personalized eco-routing information, FERPS aims to promote environmentally friendly driving behavior and contribute to overall emission reduction efforts.
Does Eco-Routing Even Work? Some Experimental Findings
Lect.Notes Mobility
Transport Research Arena Conference ; 2024 ; Dublin, Ireland April 15, 2024 - April 18, 2024
Transport Transitions: Advancing Sustainable and Inclusive Mobility ; Chapter : 9 ; 59-65
2025-05-03
7 pages
Article/Chapter (Book)
Electronic Resource
English
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