A Decision Support System for Placing Shared E-Scooters: A Case Study for Istanbul

Abstract

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.

A Sustainable Transport System—The MMQUAL Model of Shared Micromobility Service Quality Assessment

“…The first electric scooter rentals were launched by Bird in Santa Monica, USA in 2017 [33]. Since then, companies such as Bird, Lime, Bolt, and Lyft have been operating in many cities around the world [2,5,37]. At the end of 2019, e-scooter sharing was available in almost three hundred urban centres [24], including in such major European cities as Paris [38], Madrid, Vienna [39], Brussels [40], Berlin, and Stockholm [5], as well as in New Zealand [41] and Australia [42].…”

“…Among the most frequently mentioned logistical problems are congestion, air pollution from car exhaust [1,2], an insufficient number of parking spaces, poor road safety standards, and high noise levels [2][3][4]. All of these factors contribute to a poorer quality of life for urban inhabitants [5]. While transport is an integral part of the economic activity and social life of city dwellers [6], it is also one of the largest sources of air pollution in urban areas [7,8], which in turn has adverse effects on human health, the climate, and ecosystems [9].…”

Optimized Selection of Motorcycle Battery Swapping Stations Under Flexible Demand by Using Distance Function And Gis Technique

“…They also used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank appropriate criteria for station selection. Moralıoglu et al [11] created the Super Decisions program for e-scooter sharing in Istanbul, Turkey, using AHP under changing circumstances, considering factors like traffic density, uphill rate, population ratio of young people, and pedestrian traffic density. Anthopoulos and Kolovou [12] employed AHP to determine the conditions for setting up electric charging stations in Greece.…”