Exploring Shared-Bike Travel Patterns Using Big Data: Evidence in Chicago and Budapest
(2019) In Lecture Notes in Geoinformation and Cartography p.53-68- Abstract
- Bike-sharing systems are an emerging form of sharing-mobility in many
cities worldwide. The travel patterns of users that take advantage of smart devices to ride a shared-bicycle in two large cities (Chicago and Budapest) have been investigated, with analysis of approximately two million transaction data records associated with bike trips made over a three-month period in each location. Several aspects of user travel behavior—such as day and time of travel, frequency of usage, duration of usage, seasonal and peak/off-peak variations, major origin/destinations—have been included in this analysis. The results show that in both cities the bike-sharing option is a male-dominated alternative, particularly welcomed by younger groups, with... (More) - Bike-sharing systems are an emerging form of sharing-mobility in many
cities worldwide. The travel patterns of users that take advantage of smart devices to ride a shared-bicycle in two large cities (Chicago and Budapest) have been investigated, with analysis of approximately two million transaction data records associated with bike trips made over a three-month period in each location. Several aspects of user travel behavior—such as day and time of travel, frequency of usage, duration of usage, seasonal and peak/off-peak variations, major origin/destinations—have been included in this analysis. The results show that in both cities the bike-sharing option is a male-dominated alternative, particularly welcomed by younger groups, with the largest share of trips occurring in the afternoon peak. Appropriate usage of opensource big-data provides important lessons for successful vehicle sharing models,
allowing the application of the findings to other cities and mobility options where
these systems are still developing. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/09ecbd53-1a3a-46ed-bb1d-d1d6174720f8
- author
- Soltani, Ali ; Mátrai, Tamás ; Camporeale, Rosalia LU and Allan, Andrew
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bike-sharing systems, Big data, User travel behavior, Mobility
- host publication
- Computational Urban Planning and Management for Smart Cities
- series title
- Lecture Notes in Geoinformation and Cartography
- edition
- 1
- pages
- 53 - 68
- publisher
- Springer International Publishing
- external identifiers
-
- scopus:85065901744
- ISSN
- 1863-2351
- ISBN
- 978-3-030-19424-6
- 978-3-030-19423-9
- DOI
- 10.1007/978-3-030-19424-6
- language
- English
- LU publication?
- yes
- id
- 09ecbd53-1a3a-46ed-bb1d-d1d6174720f8
- date added to LUP
- 2019-05-15 09:03:49
- date last changed
- 2024-06-25 12:30:27
@inbook{09ecbd53-1a3a-46ed-bb1d-d1d6174720f8,
abstract = {{Bike-sharing systems are an emerging form of sharing-mobility in many<br/>cities worldwide. The travel patterns of users that take advantage of smart devices to ride a shared-bicycle in two large cities (Chicago and Budapest) have been investigated, with analysis of approximately two million transaction data records associated with bike trips made over a three-month period in each location. Several aspects of user travel behavior—such as day and time of travel, frequency of usage, duration of usage, seasonal and peak/off-peak variations, major origin/destinations—have been included in this analysis. The results show that in both cities the bike-sharing option is a male-dominated alternative, particularly welcomed by younger groups, with the largest share of trips occurring in the afternoon peak. Appropriate usage of opensource big-data provides important lessons for successful vehicle sharing models,<br/>allowing the application of the findings to other cities and mobility options where<br/>these systems are still developing.}},
author = {{Soltani, Ali and Mátrai, Tamás and Camporeale, Rosalia and Allan, Andrew}},
booktitle = {{Computational Urban Planning and Management for Smart Cities}},
isbn = {{978-3-030-19424-6}},
issn = {{1863-2351}},
keywords = {{Bike-sharing systems; Big data; User travel behavior; Mobility}},
language = {{eng}},
pages = {{53--68}},
publisher = {{Springer International Publishing}},
series = {{Lecture Notes in Geoinformation and Cartography}},
title = {{Exploring Shared-Bike Travel Patterns Using Big Data: Evidence in Chicago and Budapest}},
url = {{http://dx.doi.org/10.1007/978-3-030-19424-6}},
doi = {{10.1007/978-3-030-19424-6}},
year = {{2019}},
}