Intelligent Transport Greener Future 2025

Images: Getty Images Disclaimer This document is published by the World Economic Forum as a contribution to a project, insight area or interaction. The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders. © 2025 World Economic Forum. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, or by any information storage and retrieval system.Contents Reading guide 3 Foreword 4 Executive summary 5 Scope of this paper 6 Introduction 7 1 Enhancing operational efficiencies 10 1.1 Operational efficiency #1: dwell time optimization 12 1.2 Operational efficiency #2: route optimization 13 1.3 Operational efficiency #3: driver behaviour 13 1.4 Operational efficiency #4: asset maintenance 14 2 Improving capacity utilization 15 2.1 AI can help address empty capacity and reduce emissions 16 3 Optimizing modal shifts 18 3.1 Shifting freight to lower-carbon modes of transport 19 can reduce emissions 3.2 Key challenges with modal shifts and potential solutions 20 3.3 Use of predictive analytics to enable modal shifts 21 4 Critical actions needed to embrace the AI opportunity 22 4.1 Behaviour change is key to maximizing the impact of AI 23 4.2 Collaboration across the freight logistics ecosystem is crucial 23 4.3 Integrating AI needs vision from leadership and bottom-up action 25 Conclusion 29 Annex 1: Methodology 30 Contributors 31 Endnotes 33 Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 2