Bike sharing system requires prediction of bike usage based on usage history to re-distribute bikes between stations. In this study, original data was collected around Washington D.C. area in 2011 and 2012. Original data is processed by several feature engineering approaches based on analysis and understanding of the data. Ridge linear regression, support vector regression (εSVR), random forest...

The growing popularity of bike-sharing systems around the world has motivated recent attention to models and algorithms for the effective operation of these systems. Most of this literature focuses on their daily operation for managing asymmetric demand. In this work, we consider the more strategic question of how to (re-)allocate dock-capacity in such systems. We develop mathematical formulati...

In this work, we present a new framework to discover the daily mobility routines which are contained in a real-life dataset collected from a bike-sharing system. Our goal is the discovery and analysis of mobility patterns which characterize the behavior of the stations of a bike-sharing system based on the number of available bikes along a day. An unsupervised methodology based on probabilistic...

Abstract Accurate demand prediction of bike-sharing is an important prerequisite to reducing the cost scheduling and improving user satisfaction. However, it a challenging issue due stochasticity non-linearity in systems. In this paper, model called pseudo-double hidden layer feedforward neural networks proposed approximately predict actual demands bike-sharing. Specifically, overcome limitatio...

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrie...

This paper presents a new conceptual approach to improve the operational performance of public bike sharing systems using pricing schemes. Its methodological developments are accompanied by experimental analyses with bike demand data from Capital Bikeshare program of Washington, DC. An optimized price vector determines the incentive levels that can persuade system customers to take bicycles fro...

We study the problem of future bike availability prediction of a bike station through the moment analysis of a PCTMC model with time-dependent rates. Given a target station for prediction, the moments of the number of available bikes in the station at a future time can be derived by a set of moment equations with an initial set-up given by the snapshot of the current state of all stations in th...

In bike-sharing systems, a small percentage of the bicycles become unusable every day. Currently, there is no reliable on-line information that indicates the usability of bicycles. We present a model that estimates the probability that a specific bicycle is unusable as well as the number of unusable bicycles in a station, based on available trip transaction data. Further on, we present some inf...