Large-scale social digital twinning projects are complex with multiple objectives. For example, a social digital twinning platform for innovative last-mile delivery solutions may aim to assess consumer delivery method choices within their social environment. However, no single tool can achieve all objectives. Different simulators exist for consumer behavior and freight transport. Therefore, we propose a high-level architecture and present a blueprint for a generic modelling framework. This includes defining modules, input/output data, and interconnections, while addressing data suitability and compatibility risks. We demonstrate the framework’s effectiveness with two real-world case studies.

As the rapid growth of urban e-commerce increases the volume of last-mile deliveries, logistics service providers have difficulty in meeting the demand of on-demand consumer requests. This increase in demand challenges traditional delivery, with some parcels becoming disproportionately costly to deliver to their destinations. To address this, we introduce a cost-based outlier parcel selection mechanism that identifies parcels with a high negative impact on the marginal delivery costs. These outlier parcels are then eliminated from their tours and outsourced to a crowdshipping market, where individuals combine the delivery task with their already planned trips. We use unique data on delivery tours of six service providers for the province of South Holland in the Netherlands. The cost-based decision rule for identifying outlier parcels results in a low proportion of outsourcing to the crowdshipping market compared to earlier literature. We identify only about 1 % of the total parcel demand as outliers across all carriers combined. Of these outlier parcels, the proportion selected for crowdshipping based on their cost efficiency ranges from 42.78 % to 3 %, depending on the scenario. While crowdshipping provides a viable solution for handling a small portion of last-mile deliveries, its environmental and economic sustainability is restricted by factors such as compensation rates and the delivery mode used. This study demonstrates that outsourcing high-cost outlier parcels to crowdshipping can be cost-efficient and reduce emissions of last-mile logistics companies; however, the proportion of these parcels is very small, limiting the overall impact on sustainability.

This paper presents the first results of a Delphi survey aimed at eliciting experts' opinions on possible scenarios of integration between passenger and freight transport enabled by a Mobility as a Service (MaaS) platform. Combining freight shipments with passenger trips is a promising addition to the MaaS business model that could help to reduce the number of freight vehicles and contribute to a more efficient use of passenger transport services and modes. The research objective of this paper is therefore to explore the feasibility of an extended version of MaaS including freight, called “MaaS for Passenger and Freight” (MaaS4PaF). Passenger and freight transport experts were asked to express their opinions with respect to the potential market penetration, business ecosystem, and implementation of this concept. Results allow to elaborate on opportunities and barriers, especially related to uncertain business models and the multitude of actors involved, and propose a research agenda to further investigate the feasibility and potential of MaaS for passenger and freight transport integration.

Logistics plays a crucial role in modern society, particularly in densely populated urban areas, facilitating the transportation of goods. Last-mile e-commerce deliveries are emissions-intensive, contributing significantly to CO2 levels and traffic congestion. Addressing this challenge requires systemic changes in last-mile delivery ecosystems. Based on this observation, in alignment with the EU decarbonisation goals, the URBANE project (GA101069782) aims to promote the adoption of sustainable and environmentally friendly last-mile delivery solutions by introducing a collaborative layered “Platform as a Service” (PaaS) paradigm. The initiative focuses on establishing Physical Internet (PI) inspired interventions combined with the implementation of innovative tools, such as agent-based and AI models, employing a Digital Twin platform addressing the operational and strategic planning challenges of city logistics networks. A multi-factorial impact assessment radar further enhances the evaluation of the PI interventions’ effectiveness. The platform fosters collaboration among urban logistics stakeholders governed through “green” smart contracts, addressing security and privacy concerns by using a blockchain infrastructure and digital IDs, creating a trustworthy system for collaboration. The paper showcases the applicability of the URBANE Innovation Transferability Platform in designing, measuring, testing, and validating targeted logistics interventions in Lighthouse Living Labs. Cities and logistic operators receive suggestions for informed data-driven decision-making coupled with integrated and transferable applications that can be standardised and structured, aligned with the targets set in a citie’s Sustainable Urban Logistics Plan (SULP).

In the contemporary fast-evolving urban freight landscape, policymakers, planners and freight operators are confronted with increasing complexity and reduced time to act in an informed way. Traditionally, models and ex-post data analytics were the key to provide information, but demand has grown for interactive and flexible models. Digital Twins (DTs) are widely seen as an important part of the solution. The chapter describes the recent evolution of information provision in urban freight transport, paints an outlook for the future of DTs and discusses necessary conditions for their realisation. DTs provide value by visualising the current urban system and predicting future states in function of relevant external influences and actions from stakeholders. As a relatively recent requirement, they should be able to reflect the different interactions between stakeholders including the temporal scales of decision making in logistics, and its impacts. However, to be able to forecast future states, such tools need to be built with the participation of their potential users that is, all stakeholders involved in urban logistics. In the case of complex tools like DTs, the process of development is as important as prediction capabilities, to make sure they accurately reflect the trade-offs present in decision-making processes.

Urban logistics is one of the key elements of urban mobility planning. The use of real-time information systems in logistics operations generates an enormous amount of data, nowadays used mainly for the purpose of monitoring and control of large flows of goods. At the same time, urban planners, business stakeholders, and city administrators are in need of adaptive, data-driven decision support solutions to address today's urban logistics problems. Recently, digital twins have received a lot of attention to support advanced experimentation, simulation and decision-making for on-demand logistics operations. Questions still remain on how to realize these for urban logistics management in a mixed public-private stakeholder context. We argue that this lack of a specific framework for city logistics with a model library for data mergers, linking physical and virtual data exchange, can compromise the timely adoption of digital twin technology. We contribute to filling this gap by presenting a systematic review of the literature, proposing a conceptual framework for digital twin applications in urban logistics, and providing use case scenarios for their demonstration. Together, these should advance the technical implementation of digital twins in a sustainable city logistics context.

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.