Introduction 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. Methods In the present paper, a genetic algorithm approach is presented. Since data from electric vehicles usage are still scarce, origin -destination data of conventional vehicles are used and the necessary assumptions to predict electric vehicles' penetration in the years to come are made. The algorithm and a user-friendly tool have been developed in R and tested for the city of Thessaloniki. ResultsThe results indicate that 15 stations would be required to cover 80% of the estimated electric vehicles charging demand in 2020 in the city of Thessaloniki and their optimal locations to install them are identified. Conclusions The tool that has been developed based on the genetic algorithm, is open source and freely available to interested users. The approach can be used to allocate charging stations at high-level, i.e. to zones, and the authors encourage its use by local authorities of other cities too, in Greece and worldwide, in order to deploy a plan for installing adequate charging infrastructure to cover future electric vehicles charging demand and reduce the electric vehicle Bdriver anxiety( i.e. the driver's concern of running out of battery) encouraging the widespread adoption of electromobility.

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Within the last decades, the examination and definition of factors affecting the mode choice decision on school trips has gained much of attention, as the completion of such trips represent a vast percentage of total travel demand. Key players of the decision process are students' parents, deciding how their children will complete everyday trips from their residence to the school unit and vice versa. The current study examines the factors affecting parents' travel mode choice for school trips of both primary and high school students in Thessaloniki city, Greece. Data collected is based on a questionnaire survey in which, 512 parents participated, stating their perception regarding the use of several transport modes for school trips and the motives behind specific adopted travel behavioural aspects. Three main topics are examined and analysed related to the parents' attitudes and their travel habits in the choice of motorized and non-motorized transport modes, the parents' perception regarding the built environment safety, and the parents' perception regarding specific parameters which appear to motivate them in the mode choice decision process. For the research analysis, a number of statistical methods and techniques are deployed, starting with descriptive statistical and Pearson's correlation analysis and proceeding with the exploratory and confirmatory factor analysis. The results verify initial thoughts for critical factors which appear to affect parents' choices regarding their children’s school trips while they also gives an initial picture of parents' experiences regarding the school travel mode choice, in an urban environment of a typical Greek city.

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