In recent years, European countries have been trying to cultivate electrical scooters (e-scooters) as an alternative form of micro-mobility. The purchase and maintenance costs of private e-scooters are expensive so cities have been collaborating with companies to construct an e-scooter rental network. This paper aims to interpret the usage patterns of e-scooters and suggest optimal station locations for charging and parking. The dataset depicts a three-month time period (August, September, and May of the year 2020/2021) of 26,374 trips, in which there were no pandemic limitations. The results are based on two main features: the initial–final position of the e-scooter (the total distance is unknown) and the travel time. Most e-scooter rides refer to leisure trips but there is a portion of users that ride e-scooters for casual trips or commutes to and from work. Here, an electrical scooter network that covers the demand of the city of Thessaloniki is suggested. The implementation plan indicates three stages of construction up to the completion of the network. The construction operation is responsible for the creation and development of a sustainable network by adding approximately 20 stations at every stage. The capacity of the stations fluctuates between 3 and 20 parking spaces, which should accommodate e-scooters and preserve free spaces for potential riders. According to the characteristics of the city of Thessaloniki, its coverage requires the construction of 67 carefully distributed stations.

Decisions on transport policy measures have long-term and important impacts on the economy, environment and society. Transport policy measures can lock up capital for decades and cause manifold external effects. In order to allow policymakers to evaluate transport policies, the developed decision support tool facilitates the evaluation of the multidimensional impacts of the implementation of transport policies. The objective of the decision support toolset presented in this paper is to support transportation planning and design practices based on an integrated transportation analysis of the area of examination to determine the most applicable combination of mobility services. This paper provides a comprehensive description of the interactive decision support tool implemented to help cities and decision makers design their strategies and shape the urban mobility of the future.

This paper examines the distribution structure of a nationwide HoReCa company, appraising several distribution process modifications in line with the physical internet concept. Primarily, the study considers the service of electric vehicles with flexible cargo space and the utilization of a shared consolidation center within urban areas. The research leverages real-world operation data to evaluate the impact at the city and company level, and assess the effectiveness of these business models across four distinct scenarios. A digital twin incorporating customer behavior, operational choices, and stochastic events replication provides a fresh framework for evaluating shared last-mile logistics business models. Key contributions encompass results and managerial insights for implementing modular vehicles in HoReCa distribution systems, design elements for digital twins in shared urban consolidation and delivery systems, and a unified perspective for decision-making blending public and private sector needs.

As ports evolve to meet sustainability targets, seamless coordination between road and rail operations becomes fundamental to success. This study addresses the Train Loading Planning Problem (TLPP) which focuses on assigning outbound containers to train wagons under slot, weight, and pattern constraints aiming to examine its broader systemic implications. A compact mixed-integer programming formulation is developed and enhanced through a column-generation approach that efficiently prices feasible wagon plans. The optimization module is embedded within a discrete-event simulation of terminal processes including yard handling, gate operations, and train timetables. The study tests a TLPP-based rail planning algorithm within a DES of terminal and hinterland operations to quantify the impact under realistic variability. Using operational data from the Port of Valencia, realistic planning scenarios are evaluated across varying demand mixes and train frequencies. Results indicate that integrating rail capacity with optimized wagon loading reduces set-up time by 20%, delivery lead time by 54%, container dwell time by 80%, and greenhouse gas emissions by 54% compared with a trucking forwarding baseline, while maintaining throughput and alleviating congestion at terminal gates and yards. From a computational perspective, the column-generation approach achieves improved runtimes to the compact MIP and scales linearly to the number of variables. The proposed framework delivers ready to use load plans and practical insights for the deployment of additional rail capacity, supporting sustainable logistics in port environments.

The Shift2Zero project (GA 101192375) is a Horizon Europe research initiative aimed to accelerate the transition to zero-emission logistics through right-sized, mission-focused N1 e-LCVs. Within this project, a robust data validation methodology has been established to support the project’s digital and impact assessment components. The methodology begins by defining and consolidating requirements from the project’s pilots, focusing on data structure and semantics. It integrates diverse, multifunctional data sources, including operational (e.g. trip planning, travel and stop times, driver rest periods), economic (e.g. vehicle total cost of ownership components), and technical (e.g. battery autonomy, charging time, payload, and freight-specific temperature requirements). Business Process Model (BPM) diagrams are developed for each use case to map involved actors and operational workflows. This process follows enhancing KPI analytics by structuring data to feed the impact assessment framework. Input from all pilot sites and end-users is used to define relevant data models. This is achieved through the creation of entity-relationship diagrams per pilot, detailing data classes, attributes, and relationships. These models are aligned with the functional needs of the S2Z Platform and will support upcoming large-scale simulation activities. The approach ensures both interoperability and scalability of the data architecture across pilots and use cases.

This study develops a discrete-event simulation (DES) of a prospective metro-enabled last-mile delivery system integrating warehouse dispatch, rail transpo