Poor logistics efficiency, due to low load factors caused by high demand fragmentation, will have relevant negative consequences for cities in terms of pollution, congestion and overall city liveability. Policy-makers should equip themselves with appropriate tools to perform reliable, comprehensive and timely analyses of urban logistics scenarios, also considering upcoming (i) technological changes, (ii) business model evolutions and (iii) spatial-temporal changes these innovations will produce. This paper discusses the Digital Twin (DT) concept, illustrating the role it might play and clarifying how to properly conceive it with respect to urban freight transport policy-making and planning. The main message is that without a sound theory and knowledge with respect to the relationships linking contextual reality and choice/behaviour, it is not possible to make sense of what happens in the real world. Therefore, the joint use of behavioural and simulation models should characterise a DT within a Living Lab approach so to stimulate effective, well-informed and participated planning processes, but also to forecast both behaviour and reactions to structural changes and policy measures implementations.

The rapid growth of e-commerce and the pandemic-driven surge in deliveries have intensified the challenges last-mile logistics poses to urban areas. Road transport, the predominant delivery mode, is a major contributor to greenhouse gas emissions. Despite a downward trend since 2008, emissions rose in 2022, reflecting an increased mobility demand. Light commercial vehicles and trucks impact air and noise pollution due to their high emissions and noise levels. Innovative solutions, such as cargo bikes (CBs), have emerged as sustainable alternatives to mitigate these effects. This paper reports a brief literature review on CBs and evaluates their environmental, economic, and social benefits by comparing real-life data from a shipping company operating with CBs in central Rome to simulated data for motorized delivery vehicles. By analyzing their potential to reduce emissions, improve urban livability, and lower operational costs, this study seeks to raise awareness on CBs’ sustainability as a viable alternative for last-mile logistics. Highlighting these advantages can support policymakers, businesses, and urban planners in fostering a transition to more sustainable urban mobility solutions.

The paper considers models for best, worst and best-worst choice probabilities, that use a single common set of random utilities. Choice probabilities are derived for two distributions of the random terms: i.i.d. extreme value, i.e. Logit, and multivariate normal, i.e. Probit. In Logit, best, worst and best-worst choice probabilities have a closed form. In Probit, worst choice probabilities are simply obtained from best choice probabilities by changing the sign of the systematic utilities. Strict log-concavity of the likelihood, with respect to the coefficients of the systematic utilities, holds, under a mild necessary and sufficient condition of absence of perfect multicollinearity in the matrix of alternative and individual characteristics, for best, worst and best-worst choice probabilities in Logit, and for best and worst choice probabilities in Probit. The assumption of substitutability between best and worst choices is tested with data on mode choice, collected for the assessment of user responses to urban congestion charging policies. The numerical results suggest significantly different preferences between best and worst choices, even accounting for scale differences, in both Logit and Probit models. Worst choice data exhibit coefficient attenuation, less pronounced in Probit than in Logit, and higher mean values of travel time savings with larger confidence intervals.