As part of the overall goal of carbon emissions reduction, European cities are expected to encourage the electrification of urban transport. In order to prepare themselves to welcome the increased number of electric vehicles circulating in the city networks in the near future, they are expected to deploy networks of public electric vehicle chargers. The Electric Vehicle Charging Infrastructure Location Problem is an optimization problem that can be approached by linear programming, multi-objective optimization and genetic algorithms.

The improvement of urban mobility has become a key concern to all institutions and researchers dealing with transport research. It was the vital need to change the “culture” of urban mobility promoting environmentally friendly modes of transport and the rational use of private car that forwarded Urban Mobility Centres in Greece and other countries. A modern and advanced Urban Mobility Centre for the city of Thessaloniki is under way and it is expected to offer to the citizens various services together with a Traffic Control Centre by the end of 2011. An innovativeness of the centre is the specialized service providing environmentally friendly routing, taking into account specific air pollutants, as the impact of the use of vehicles on the atmosphere, and existing -dynamically calculated - environmental conditions of the wider area. The paper deals with the need to organize such an Urban Mobility Centre. The methodology to approach the problem, in order to design the centre's services, is presented, as well as the expected contribution to the environmentally upgrading of the Metropolitan area of Thessaloniki.

Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app.