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...

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations ...

Over the past decades, Bike-Sharing Systems have been implemented in hundreds of cities all around world. During this time, numerous academic studies published with analyses from diff...