Next Article in Journal
Recent Sea Level Change in the Black Sea from Satellite Altimetry and Tide Gauge Observations
Next Article in Special Issue
GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data
Previous Article in Journal
Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin
Previous Article in Special Issue
On the Right Track: Comfort and Confusion in Indoor Environments
 
 
Article

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

by 1,2, 3 and 4,5,*
1
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Department of Geography, College of Science, Swansea University, Swansea SA28PP, UK
3
School of Soil and Water Conservation, Beijing Forest University, Beijing 100083, China
4
Zhou Enlai School of Government, Nankai University, Tianjin 300350, China
5
Computational Social Science Laboratory, Nankai University, Tianjin 300350, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(3), 184; https://doi.org/10.3390/ijgi9030184
Received: 18 February 2020 / Revised: 19 March 2020 / Accepted: 23 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston. View Full-Text
Keywords: Uber Movement; Travel time; Commuting time; Origin-destination Matrix; Aggregate data Uber Movement; Travel time; Commuting time; Origin-destination Matrix; Aggregate data
Show Figures

Figure 1

MDPI and ACS Style

Sun, Y.; Ren, Y.; Sun, X. Uber Movement Data: A Proxy for Average One-way Commuting Times by Car. ISPRS Int. J. Geo-Inf. 2020, 9, 184. https://doi.org/10.3390/ijgi9030184

AMA Style

Sun Y, Ren Y, Sun X. Uber Movement Data: A Proxy for Average One-way Commuting Times by Car. ISPRS International Journal of Geo-Information. 2020; 9(3):184. https://doi.org/10.3390/ijgi9030184

Chicago/Turabian Style

Sun, Yeran, Yinming Ren, and Xuan Sun. 2020. "Uber Movement Data: A Proxy for Average One-way Commuting Times by Car" ISPRS International Journal of Geo-Information 9, no. 3: 184. https://doi.org/10.3390/ijgi9030184

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop