TY - CHAP
T1 - Exploring Shared-Bike Travel Patterns Using Big Data
T2 - Evidence in Chicago and Budapest
AU - Soltani, Ali
AU - Mátrai, Tamás
AU - Camporeale, Rosalia
AU - Allan, Andrew
PY - 2019/5/10
Y1 - 2019/5/10
N2 - 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 open-source 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.
AB - 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 open-source 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.
UR - https://doi.org/10.1007/978-3-030-19424-6
U2 - 10.1007/978-3-030-19424-6_4
DO - 10.1007/978-3-030-19424-6_4
M3 - Chapter
SN - 9783030194239
SN - 9783030194246
T3 - Lecture Notes in Geoinformation and Cartography ((LNGC))
SP - 53
EP - 68
BT - Computational Urban Planning and Management for Smart Cities
A2 - Geertman, Stan
A2 - Zhan, Qingming
A2 - Allan, Andrew
A2 - Pettit, Christopher
CY - Cham, Switzerland
ER -