relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2596/
title: Multi-source Data Analysis for Bike Sharing Systems
creator: Nguyen, Thi Hoai Thu
creator: Le, Trung Thanh
creator: Chu, Thi Phuong Dung
creator: Nguyen, Linh Trung
creator: Le, Vu Ha
subject: Electronics and Communications
subject: Information Technology (IT)
description: Bike sharing systems (BSSs) have become common in many cities worldwide, providing a new transportation mode for residents’ commutes. However, the management of these systems gives rise to many problems. As the bike pick-up demands at different places are unbalanced at times, the systems have to be rebalanced frequently. Rebalancing the bike availability effectively, however, is very challenging as it demands accurate prediction for inventory target level determination. In this work, we propose two types of regression models using multi-source data to predict the hourly bike pick-up demand at cluster level: Similarity Weighted K-Nearest-Neighbor (SWK) based regression and Artificial Neural Network (ANN). SWK-based regression models learn the weights of several meteorological factors and/or taxi usage and use the correlation between consecutive time slots to predict the bike pick-up demand. The ANN is trained by using historical trip records of BSS, meteorological data, and taxi trip records. Our proposed methods are tested with real data from a New York City BSS: Citi Bike NYC. Performance comparison between SWK-based and ANN-based methods is provided. Experimental results indicate the high accuracy of ANN-based prediction for bike pick-up demand using multi-source data.
date: 2017-10-18
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2596/1/1570380069.pdf
identifier:   Nguyen, Thi Hoai Thu and Le, Trung Thanh and Chu, Thi Phuong Dung and Nguyen, Linh Trung and Le, Vu Ha  (2017) Multi-source Data Analysis for Bike Sharing Systems.  In: 2017 International Conference on Advanced Technologies for Communications (ATC), 18-20 October 2017, Quy Nhon, Viet Nam.