Urban areas are growing rapidly, and with them come familiar challenges: air and noise pollution, traffic congestion, parking shortages, and safety concerns. In this context, freight transport has become increasingly fragmented due to e-commerce, and as a result, it lacks coordination across supply chains. To address these challenges, a new study presents a co-creation-based decision support tool designed to optimize sustainable alternatives to road-based urban logistics.
Rethinking Urban Freight with AI
At the heart of this initiative is the integration of Artificial Intelligence (AI) into urban logistics planning. AI methods (such as expert systems, multi-agent systems, and machine learning) enable smarter logistics tools that simulate decision-making, optimize traffic flows, and even predict greenhouse gas (GHG) emissions. These tools are often embedded within digital twins, virtual models of cities that help stakeholders test logistics scenarios before they’re implemented in the real world.
Machine learning and deep learning techniques also support tasks such as classifying delivery requests and modeling emissions from different transportation modes, including road, rail, and waterborne freight. These technologies enable a shift from reactive to predictive logistics management, improving responsiveness and sustainability.
Sustainable Urban Logistics: More Than Just Technology
While technology provides powerful tools, the success of sustainable city logistics also depends on human-centered design. That’s where co-creation comes in. This approach emphasizes collaboration between logistics providers, city authorities, local businesses, and citizens. By integrating stakeholder input throughout the design and implementation process, logistics solutions are more likely to meet real-world needs and gain broad support.
The paper proposes that co-creation, supported by new communication technologies and big data, can help balance the economic interests of businesses with the environmental and social goals of cities. For example, while rail or river transport may be less cost-effective than trucks, their lower emissions and noise levels make them viable alternatives in dense urban environments.
A Test Case in “Grand Paris Sud”
The proposed AI-powered decision support tool is being tested in the Grand Paris Sud region. Here, stakeholders from the public and private sector are involved in defining use cases, setting performance indicators, and validating results. This collaborative testing phase will help refine the tool’s ability to simulate real-life logistics scenarios and weigh economic, environmental, and social outcomes.
Conclusion
The study highlights that the future of urban logistics lies in combining advanced AI methods with participatory, co-creative processes. By bringing together citizens, companies, and policymakers in the early stages of solution development, cities can foster smarter, greener logistics systems that serve both people and the planet.