The COVID-19 pandemic had a significant effect in urban mobility, while essential changes are being observed in travelers’ behavior. Travelers in many cases shifted to other transport modes, especially walking and cycling, for minimizing the risk of infection. This study attempts to investigate the impact that COVID-19 had on travelers’ perceptions towards bike-sharing systems and whether the pandemic could result in a greater or lesser share of trips that are being conducted through shared bikes. For that reason, a questionnaire survey was carried out in the city of Thessaloniki, Greece, and the responses of 223 people were analyzed statistically. The results of the analysis show that COVID-19 will not affect significantly the number of people using bike-sharing for their trips. However, for a proportion of people, bike-sharing is now more attractive. Moreover, the results indicate that bike-sharing is now more likely to become a more preferable mobility option for people who were previously commuting with private cars as passengers (not as drivers) and people who were already registered users in a bike-sharing system. The results also provide evidence about the importance of safety towards COVID-19 for engaging more users in bike-sharing, in order to provide them with a safe mobility option and contribute to the city’s resilience and sustainability.

The use of participatory techniques in the field of transport is coming to the forefront recently. In this frame, eight co-creation workshops and five online crowdsourcing campaigns took place in Thessaloniki, Southern Tuscany, Rotterdam/The Hague, and Frankfurt, from which many innovative ideas to enhance Public Transport were generated by citizens. A simple list of innovations would not be very useful for Public Transport Operators, as they cannot implement all of them at once. There was an obvious need for their ranking and this is the purpose of this paper. The ranking was realized with the most used Multi-Criteria Decision Analysis method in transportation research, i.e. the Analytic Hierarchy Process, using three criteria: Feasibility, Utility, and Innovativeness. An online questionnaire was distributed to experts, using a modified snowball sampling technique, which yielded 97 completed questionnaires. Utility (42.90%) was found to be the most important criterion, followed by Feasibility (40.10%), and Innovativeness (17.00%). Four lists of innovations were derived, ranked with respect to a) all three examined criteria, b) Feasibility, c) Utility, and d) Innovativeness. The highest ranked innovation for a) and c) was found to be Mobility as a Service and platform with real-time travel, comfort, and multi-modal information; for b) City marketing from a Public Transport perspective; and for d) Advanced e-ticketing system. The results revealed which of the innovations are the most promising and provide valuable insight into how to integrate innovation with Public Transport to make it more attractive. Public Transport Operators may use the results according to the peculiarities of their city and the importance they give to Feasibility, Utility, and Innovativeness.

Towards the achievement of climate neutrality in the cities, the GHG emissions in transport are expected to be reduced by 90% until 2050, while the acceleration of the introduction of innovative mobility services enabled by digital platforms in the cities facilitate the achievement of the goal. The development of an integrated e-service for vehicle sharing would assist cities towards climate neutrality and digital transition. The integrated e-service aims to assist the management and the use of shared e-vehicles and consisted of both web service application and business intelligence platform for data analysis. Through the collection and analysis of data derived from both vehicles and rental system, the data analytics platform offers decision support tools based on machine learning algorithms.

As supply chains extend geographically, the supply chain management seems crucial for improving companies’ activities and enhancing their competitiveness. This global market liberalization creates an uncertain environment for fulfilling the deliveries on time based on the complex supply chain environment consisting of different companies acting in various stages of the supply chain process. In this context, lead time is one of the most critical measures for indicating a supply chain performance, as lead time forecast accuracy is a crucial factor of mitigating the bullwhip effect. Within the framework of the ESCALATOR project, a new innovative service is being developed aiming to dynamically forecast lead time variation, through the use of historical data and dynamic forecasts of lead time delays at the previous stages of a supply chain. This paper focuses on the validation and final selection of the factors causing delays in various stages of a supply chain, as well as the most suitable machine learning models in order to develop a service for dynamic lead time predictions at different stages of a supply chain.