A smart and sustainable city should be an innovative city that uses information and communication technologies to improve the quality of life via its operations. They need to be planned, managed, and regulated by open data collected through different data sources to provide efficient services. Transportation services can be accepted as one of the essential services of a city. In smart cities, intelligent transportation systems help to solve problems such as traffic congestion or the amount of fuel spent in traffic by providing communication between vehicles and devices that build the whole transportation network. In order to achieve the success of intelligent transportation systems, transportation methods should be planned dynamically according to the collected data and the requirements of the city's transportation network. E-scooters are also a part of the transportation system, and since 2017, shared e-scooter systems have been used as a transportation alternative in some cities. However, e-scooters are placed in random locations in cities without relying on a precise algorithm. Thus, users in some locations cannot benefit from the e-scooter sharing system efficiently due to the lack of e-scooter in neighborhoods. In this study, a decision support system for e-scooter sharing systems is suggested, which helps to place e-scooters dynamically in areas that are needed in the city. This system is intended to offer select options by combining the traffic density information of the regions and alternative region data provided by the multi-criteria analysis made using the Analytical Hierarchy Process (AHP) with real-time social media data.