Impact of Different Stakeholders of Bike-Sharing Industry on Users’ Intention of Civilized Use of Bike-Sharing
نویسندگان
چکیده
منابع مشابه
Incentivizing Users for Balancing Bike Sharing Systems
Bike sharing systems have been recently adopted by a growing number of cities as a new means of transportation offering citizens a flexible, fast and green alternative for mobility. Users can pick up or drop off the bicycles at a station of their choice without prior notice or time planning. This increased flexibility comes with the challenge of unpredictable and fluctuating demand as well as i...
متن کاملPrediction of Bike Sharing Demand
Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with z...
متن کاملBalancing Bike Sharing Systems
Bike Sharing Systems became popular in recent years to extend the public transportation network of cities or regions. Most research in this area focuses on finding the optimal locations for bike sharing stations. Still various approaches on how to operate such a system efficiently exist. The usefulness of a bike sharing system strongly depends on the user convenience which is directly connected...
متن کاملProfiling Users of the Velo'v Bike Sharing System
Detecting and characterizing geographical areas that are attractive places for specific people, in specific contexts, is an important but challenging new problem. Mobility traces and their related circumstances can be modeled thanks to an augmented graph in which nodes denote geographic locations and edges are represented by a set of transactions that describe users’ demographic information (e....
متن کاملPrediction of Bike Sharing Systems for Casual and Registered Users
In this project, two different approaches to predict Bike Sharing Demand are studied. The first approach tries to predict the exact number of bikes that will be rented using Support Vector Machines (SVM). The second approach tries to classify the demand into 5 different levels from 1 (lowest) to 5 (highest) using Softmax Regression and Support Vector Machines. Index Terms –regression, classific...
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ژورنال
عنوان ژورنال: Sustainability
سال: 2018
ISSN: 2071-1050
DOI: 10.3390/su10051437