Today in 2019 as we move from the information age to the digital age, the possibilities of data usage may reshape the decision support systems. Social media is one of the new tools that collects valuable information like geolocation data to be used in various decision support systems for different industries. The location tags that are used in location-based social networks like Instagram and Twitter can have an impact on the studies related to urban design and planning. In this study, we present an electric scooter deployment model to reduce traffic congestion and travel time with the use of real-time data from social media in urban areas with heavy pedestrian and vehicle traffic.
In the scope of this study, a survey is conducted to estimate the e-scooter potential usage in Istanbul. The main criteria for the locations of e-scooters are collected through online questionnaires and information available from the e-scooter companies around the world. Following that, collected criteria are processed in multi-criteria decision analysis. We use the Analytic Hierarchy Process (AHP) method to weight and rank these criteria for sharing location selection. All traffic information is collected through transactions of Google Maps users and cross-checked with the survey results. The short-distance routes with the high pedestrian and vehicle traffic in the city are categorized in the decision process. In that way, the location alternatives to place e-scooters will be changing according to data coming from traffic information applications and social media. We assume that this system will offer the best locations for e-scooter placement and use. Additionally, the proposed approach can reduce commuting time, while contributing to the reduction of environmental footprint.
