Abediasl, Hamidreza (University of Alberta), Aliramezani, Masoud (University of Alberta), Koch, Charles Robert (University of Alberta), Shahbakhti, Mahdi (University of Alberta)

Monitoring Real-Time Fleet Emissions through an Intelligent Fleet Management System

Scheduled for presentation during the Regular Session "Transport planning" (FrAT9), Friday, September 27, 2024, 10:50−11:10, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

Monitoring fleet vehicle operation is a crucial aspect of fleet management systems, typically involving the analysis of GPS and/or on-board diagnostics (OBD) data from fleet vehicles. Given the increasing emphasis on environmental responsibility within fleet management, there is a growing demand for monitoring vehicle emissions. This study aims to integrate machine learning models for emission estimation into an intelligent fleet management system (IFMS) designed for light-duty vehicle fleets. These emission models are trained using real-driving data from fleet vehicles and have the capability to provide real-time emission estimates based on the OBD data of each vehicle. The developed IFMS is hardware-agnostic, allowing for wireless data collection, and enables the real-time reporting of fleet emissions, including CO2, CO, HC, and NOx.