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.
This paper addresses quality considerations in public transportation systems and, in particular, the relation between quality of public transport service and customer satisfaction. Its aim is to provide insights into the factors that affect transit-rider satisfaction and to present a model to calculate the probability of customer satisfaction. The proposed model identifies the most important attributes of public transport service quality that can be used for service planning.
IntroductionThis paper aims to provide responsive measures to various combinations of weather, light, road type and road/traffic conditions which occur during the daily transport service operations of ATC buses on bus lanes in the Province of Bologna, Italy. Each combination is assigned to a response measure according to the risk level, as this has been stated by experts. The data has been gathered from questionnaires, which have been answered by expert panels comprised of ATC bus drivers. The final risk index, in form of responsive measures, is derived from a non-parametric statistical analysis of the questionnaires results. The pseudo-code for the risk assessment algorithm has also been written and is presented in the paper. The questionnaires have been designed in a form which has been considered most suitable both from the perspective of the possibilities for the analysis of the results, as well as from the perspective of motivating the experts to answer each question carefully (since the questionnaire has been comprised of 64 questions, each having five possible answers) by relating the risk associated to each described situation to the measure that should be taken in order to eliminate the related risk. It is believed that the questionnaire with answers relating to exact measures rather than to numerical risk indexes provided a motivation to the experts to consider their answers more, since they can realize more easily that their own safety is directly concerned. Their knowledge of the exact conditions of the road network and the associated risks that they are exposed to enables the development of a robust risk assessment algorithm. The implementation of the risk assessment algorithm aims to decrease the probability of an event (accident) by enabling preventive measures, either directly related to a possible accident or indirectly leading to a high risk situation that can lead to an accident.
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