A Study on the Optimal Vehicle Deployment for Hybrid Public Electric Scooter Sharing Systems

碩士 === 國立臺灣科技大學 === 資訊管理系 === 102 === With the huge increasing with economic development, transportation in cars and motorcycles, so does the serious pollution of carbon dioxide and global warming. Therefore carbon reduction is the main topic worldwide in this century. Due to the concept of environm...

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Bibliographic Details
Main Authors: Yi-ju Lee, 李宜儒
Other Authors: Cheng-huang Hung
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/70169065551703086501
Description
Summary:碩士 === 國立臺灣科技大學 === 資訊管理系 === 102 === With the huge increasing with economic development, transportation in cars and motorcycles, so does the serious pollution of carbon dioxide and global warming. Therefore carbon reduction is the main topic worldwide in this century. Due to the concept of environmental protection, economic recession and higher oil price, green transportation has aroused more and more attentions recently,  especially by the introduction to the concept of vehicle sharing that promotes shared vehicles to conserve energy, reduce carbon emissions, and improve traffic congestions. However, for some places with bad weather or topography not so convenient for biking, electric scooters (e-scooters) may serve better than bicycles as shared vehicles, since they are as mobile as bicycles, and can move even faster with ease. In this thesis, we focuses on hybrid public electric scooter sharing systems, a rental station consist of battery charging stations and battery exchange station, we would like to put optimal number of e-scooters and battery in battery exchange station at each rental site in the beginning of each day so that the number of satisfied Origin-Destination demands attains specified service level requirement with minimum number of e-scooters. Thereby service requirement can be achieved with minimum total cost. Based on demand proportion and rental behavior in real world, we propose a integer model to mimic the actual e-scooter traffic between rental sites. and solve it by Particle Swarm Optimization (PSO) algorithm.