• Home

  • Documents

  • Leila Hawa, Boer Cui, Determinants of shared electric ... over.pdfModel 1 Model 2 Model 3 Model 4...
1
ACKNOWLEDGEMENTS The authors would like to thank the Social Sciences and Humanities Research Council (SSHRC) and the Natural Sciences and Engineering Research Council of Canada (NSERC) for their financial support. DISCUSSION CONCLUSION REGRESSION RESULTS COVARIATES Temporal MODEL DESIGN Land Use Transportation Infrastructure Weather Multi-level mixed effects regression modelling Model 1 Presence of e-scooters Model 2 Average number of e-scooters Model 3 Hourly change in average number of e-scooters Model 4 Coefficient of variation Model type Logit Linear Linear Linear Dependent variable 1 1 0 ? 12 ? ? 12 Coefficient of SD variation μ Omission None ? i = ? i-1 = 0, 12AM – 1AM CV = SD = μ = 0, 12AM – 6AM Temporal unit Hour (144) Hour (144) Hour (138) Day (6) Spatial unit Fishnet (1,671) Fishnet (1,308) Fishnet (1,306) Fishnet (1,297) No. observations 240,624 78,260 75,044 5,539 Bootstrapping Yes Yes Yes No Land Use Sociodemographic Model 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Number of Bus Stops 1.26 *** 0.06 *** 0.00 -0.02 * Number of Metro Stations 1.94 2.01 *** 0.51 *** -0.20 Number of Parking Meter Spaces 0.96 ** -0.02 ** 0.00 0.01 Number of Capital Bikeshare Stations 3.16 *** 0.83 *** 0.19 *** -0.30 *** Fishnet Contains a Bicycle Lane 2.73 *** 0.02 0.08 *** -0.21 ** Model 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Temperature(°C) 1.02 -0.04 *** 0.01 * 0.02 * Precipitation Intensity (mm/hr) 0.85 0.05 -0.14 ** 1.76 *** Humidity (0-1) 2.60 * 2.36 *** 0.18 -1.44 *** Wind Speed (km/hr) 0.99 0.03 ** 0.01 0.03 *** STUDY AREA Source: DC GIS Open Data, Census Bureau Population Density (People / Square Mile) 0 - 4408 4408 - 9418 9418 - 16845 16845 - 30101 30101 - 66789 Median Income 0 - 8785 8786 - 41301 41302 - 79815 79816 - 131571 131572 - 197155 Coordinate System: GCS WGS 1984 Datum: WGS 1984 Units: Degree ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Land Use University and College Campuses Central Business District National Parks Bodies of Water Boundary of Washington D.C. Locations of Services ! Museums ! Marketplaces ! Bars and Restaurants Study Area 0 5 10 2.5 Kilometers 0 2.5 5 1.25 Miles ´ DATA Six Full Days in 2019 Sunday May 12th Monday May 13th Tuesday May 14th Thursday May 16th Saturday June 1st Friday June 14th Sources Bird Jump Lime Lyft Skip Spin APIs - publicly accessible via DDOT Unit of Analysis Geographic .07 square miles = .19 square kilometers *p<0.05, **p<0.01, ***p<0.001 Statistical Significance Inputs Spatial: Latitude Longitude Temporal: Timestamp 5 minute - intervals 1671 fishnets x 6 days x 24 hours = 240,624 observations Model 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Weekend Day 0.79 * -0.26 * -0.16 *** -0.31 ** 12AM – 6AM 0.58 *** -0.82 *** -0.41 *** N/A N/A 6AM – 12PM 0.65 *** 0.21 -0.03 N/A N/A 12PM – 6PM 0.88 0.68 *** 0.04 N/A N/A Model 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Census Tract Population Density (1000s) 1.13 *** 0.02 *** 0.00 ** -0.02 *** Low Income Area 9.58 *** 0.27 0.05 -0.39 * Low–Medium Income Area 11.22 *** 0.35 * 0.09 -0.39 * High–Medium Income Area 17.33 *** 0.05 0.04 -0.25 Number of Museums 1.44 0.64 *** 0.22 *** -0.14 Number of Marketplaces 2.15 *** -0.31 *** -0.07 ** -0.16 * Number of Bars & Restaurants 1.16 *** 0.23 *** 0.05 *** -0.03 *** Part of the CBD 25.36 *** 3.57 *** 1.00 *** -0.63 *** Part of a College Campus 2.28 *** -0.13 -0.01 -0.10 Part of a National Park 1.12 0.14 ** 0.06 ** 0.06 INTRODUCTION INTRODUCTION Weekends & late nights: fewer e-scooters & less variation in hourly e-scooter presence. Higher population density & being located in the CBD: more e-scooters, contributed to more change in the hour-to-hour numbers of e-scooters, but less variation throughout the day. Bikeshare stations & bicycle lanes: positively impacted the presence & hourly change in e-scooters, low variation throughout the day. Metro stations: positively impacted the average number of e-scooters in an area, and hourly movement to & from an area, not a significant indication of presence. There is a relationship between public transport & e-scooters, it is not clear if they serve as a first-mile last-mile solution. The models suggest that e-scooters were availble near bike lanes . Our study examines the factors that determine the presence of e-scooters , as well as those that cause variation in e-scooter presence between each consecutive hour and throughout the day . Data on the location of e-scooters in the Washington D.C. area over six full days was collected. Then, multi-level mixed effects linear regression models were generated to investigate the impact of time , land use characteristics, and transportation infrstructure while controlling for weather conditions. This study contributes to a more comprehensive understanding of the factors that impact the presence as well as variations in the presence of e-scooters using data obtained for e-scooters operating in Washington D.C. Dataset cannot address if an e-scooter was placed as part of rebalancing or by a user. Utilization patterns can help city planners & officials understand how shared e-scooters are used and how they interact with existing systems. Leila Hawa, Boer Cui, Lijun Sun & Ahmed El-Geneidy McGill University Determinants of shared electric scooter use in Washington D.C. Scoot over: 12:00 AM - 1:00 AM 6:00 AM - 7:00 AM 12:00 PM - 1:00 PM 6:00 PM - 7:00 PM E-Scooters per Fishnet 0 - 2 3 - 8 9 - 16 17 - 26 27 - 49 Metro Lines Central Business District Bodies of Water Transportation Research at McGill RAM T Distribution of e-scooters at selected periods of the day Coordinate System: GCS WGS 1984 Datum: WGS 1984 Units: Degree Sources: District Department of Transportation ´ 0 5 10 2.5 Kilometers 0 2.5 5 1.25 Miles

Leila Hawa, Boer Cui, Determinants of shared electric ... over.pdfModel 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Temperature( C) 1.02

  • Upload
    others

  • View
    2

  • Download
    0

Embed Size (px)

Citation preview

Page 1: Leila Hawa, Boer Cui, Determinants of shared electric ... over.pdfModel 1 Model 2 Model 3 Model 4 O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig. Temperature( C) 1.02

DATA

ACKNOWLEDGEMENTSThe authors would like to thank the Social Sciences and Humanities Research Council (SSHRC) and the Natural Sciences and Engineering Research Council of Canada (NSERC) for their financial support.

DISCUSSION

CONCLUSIONREGRESSION RESULTS

COVARIATES

Tem

pora

l

MODEL DESIGN

Land

Use

Tran

spor

tatio

n In

fras

truc

ture

Wea

ther

Multi-level mixed effects regression modelling

Model 1Presence of e-scooters

Model 2Average number of

e-scooters

Model 3Hourly change in average

number of e-scooters

Model 4Coefficient of variation

Model type Logit Linear Linear Linear

Dependent variable1 1

0?

12 ? ?

12Coefficient of SD variation µ

Omission None? i = ? i-1 = 0,12AM – 1AM

CV = SD = µ = 0,12AM – 6AM

Temporal unit Hour (144) Hour (144) Hour (138) Day (6)

Spatial unit Fishnet (1,671) Fishnet (1,308) Fishnet (1,306) Fishnet (1,297)

No. observations 240,624 78,260 75,044 5,539

Bootstrapping Yes Yes Yes No

Land UseSociodemographic

Model 1 Model 2 Model 3 Model 4

O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig.

Number of Bus Stops

1.26 *** 0.06 *** 0.00 -0.02 *

Number of Metro Stations

1.94 2.01 *** 0.51 *** -0.20

Number of Parking Meter

Spaces0.96 ** -0.02 ** 0.00 0.01

Number of Capital

BikeshareStations

3.16 *** 0.83 *** 0.19 *** -0.30 ***

Fishnet Contains a

Bicycle Lane2.73 *** 0.02 0.08 *** -0.21 **

Model 1 Model 2 Model 3 Model 4

O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig.

Temperature(°C) 1.02 -0.04 *** 0.01 * 0.02 *

PrecipitationIntensity (mm/hr) 0.85 0.05 -0.14 ** 1.76 ***

Humidity (0-1) 2.60 * 2.36 *** 0.18 -1.44 ***

Wind Speed (km/hr) 0.99 0.03 ** 0.01 0.03 ***

STUDY AREA

Source: DC GIS Open Data, Census Bureau

Population Density(People / Square Mile)

0 - 44

08

4408

- 941

8

9418

- 168

45

1684

5 - 30

101

3010

1 - 66

789

Median Income

0 - 87

85

8786

- 413

01

4130

2 - 79

815

7981

6 - 13

1571

1315

72 - 1

9715

5

Coordinate System: GCS WGS 1984Datum: WGS 1984Units: Degree

!

!

!!

!

!

!! !!!!!

!!!!!!

!

!

!

!

!!

!!

!

!

!

!

!!

!!!

! !

!

!

!

!

!!

!!! !

!

!!!

!

! !

!

!!

!

!

!!

!

!

!

!

!

!

!!!!!!!!!

!

!!!

!!

!

! ! ! !!

!

!! !! !!!!!!!

!

!

!

!

!

!

!

!

!!

!

!

! !!!

!!

!

! !

!

!!

!

!

!!

!

!

!!!!!

!

!

!

!

!!

!

!

! ! !!!!!!!!

!

!

!

!!

!

!

!

!!

!

!

!!

!

!!

!

!

!

!! !

!

!! !

!

!

! !

!

!

!! !

!!

!

!

!!

!!

!!

!

!

!

!!!

!

!

!

!

!

!

!

!!

!

!!

!!!!

!!

! !

!

!

!

! !!

!

! !!!

!

!!

!

! !

!

!

!!!!!!

!

! !!

!

!!!!! !!!

!

!

!

!

!!

!

!!!! !

! !

!

!!!!!

!

!!!!

!!

!!!

!

!

!

!

!!

!!

!!

!

!

!!!

!

!

!

!!

! !

!!

!

!

!!

!!

!

!

!!! !!! !!

!

!!!!!

!

!

!

!

! !!! !!!!!!

!!

!

!! !!! !

!

!!

!

!

!!!

!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!!!! !

!

!!!

!

!

!

!

!

!

!!!

!

!

!!!

!

!!

!

!! !

!

!!! !

!!!

!

!

!!

!

!

!

!

!

!!

!

!!

!

!!

!

!!!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!! !

!

! !! ! !!

!

!

!! !

!

!!!

!! !

!

!!

!

!

!

!

!!

!

!

!

!

!!

!!! !!!!!! ! !

!

!

!

! !

!!

!!

!

!

!

!

!!

!

!

!!

!! !

!

!

!!

! !!

!

!

!!!!

!

!

!!

!

!!!

!

!!

! !

!

! ! !!

!!

!

!

! !!!!!!!!

!!!

!!!

!!

!

! !!! !

!

!

!

!! !

!!

! !!!

!

!

!!!!

!

!

!

!!!

!

!

!

! !!

!

! !! !!! !

!!

!! !!

!

! ! !!!!

!

!

!!

!!! !

!

!!!!!!!!!!! !! !

!

!

!!

!!

!!

!

!

!

!

!!

!!!!!

!

!

!! !

!

!!

!!

!

!

!

!! !!

!

!

!

!

!

!

!

!!

!!!!!

!

!!

!

!!

!

!

!

!!

!

!!!

!

!

!

!

!

!

!!

!!

!

!!!

!

!

!

!

!!

!! !

!

!!

!

!!!!

!

!

!!!

!

!!

!

!

!!

!

!

!

!!

!

!

!

!

!

!

!

! !

!

!!

!

!!

!!

!

!

! !

!

! !

!

! !!!

!

!

!! ! !!

!

!!!!

!

! !

!

! !! !!!

!

!!!!

!

! !

!

!! !

!

!!

!

! !

!

!!!!

!

! !

!

!

!

! !!

!

! !

!

!

!

!

!

!!

!

!!!

!

!

!

!

!!!

!

!

!!

!

!!

!

!

!

!

!

!

!

!

!

!!! !!

!!

!

!

!!

!!

!!!

! !!!!!! !! !!

!

!

!

!

!! !!

!

!!

!!

!

!!

!

!

!

!

!

!!

!

!

!!!!

!

!!

!

!!

!

!!

!

!!!!!

!

!

!!

!!

!!!

!

!!

!

!

!

!!

! ! !

!

!

!

!

!!!

!

!!!

!!

!

!

!

!

!

! !

!

!

!!

! !

!

!

!

!

!!

!

!

!

!

!

!

!!!!

!

!

! !! !

!

!!

!

!!!

!

!

! !

!!

!

!

!!

!

!

!

!

!!

!

!

!!

!

!!

!!

!!!!!

!

!

!

!

!

!!!!

! !

!!!!

!!

!!

!!!! ! !!! !!! !!!! !!!!!!

!

!!

!

!!

!

!

!

!

!!! !!

!

!!

!!

!

!!

!

!

!

!!

!

!!!!! !! !!!!! !

!!!!!

! !

!

!

!!

!

!

!!!!!

!

!

!

!

!

!!!!!!

!!

!

!

!!

!

!

!!

! !!! !

!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!!! !!

!

! !!!

!!

!!!!!! !

!!!

!

!

!

!

!!

!

!

!

!

!!

!!

!!!

!

!

!

!

!

! !!

!

!!!

!

!

!!

!

!!!

!

!!!

!

!! !!!

!

!

!!

!!

!! !!

!

!

!!!! ! !

!

!

!

!

! !

!

!!!!

!

!

!

!

!

!!

!

!

!

!

! !

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!!!

!

!

!!!

!!!! !

!!

!!!

!

!!!!!!! ! !! !!!

!!

! !!!!! !!

!

!

!

!!!

!

!!!!

!!

!!!

!

!!

!!

!!!!

!

! !

!

!

!

! !

!

!

!

!

!

!!!!!!!!!!!!!!!

!

!!

!

!

!

!

!!!

!

! !

!

!

!

!

!

!

!

!

!!!

!

!

!

!

!

!

!

!

!

!

!

!

! !

!

!!!!

!

!

!

!

!

!

!!

!

!!

!!

!

!

!

!!

!!!!

!

!

!

!

!!

!!!!

!!

!!!

!

!

!!

!

!

!!

!!

!!!!

!

!!

!!!

!

!!

!!

!

!

!

!

!

!!

!

!

!

!!!!

!

!!

!!

!

!!!

!

!

!

!

!

!

!

!

!!

!

!!

!

!

!

!

!!!!

! ! !

!

!

! !!!!

!!

!

!

!!

!

! !!

!!!!!!!!!

!!!!

!

!

! !!!

!!

!!!!! !!!!

!!!

!

!!!!!!!!

!!

!! !

!

!!!!

!!

!

!

!!

! !

!!!!!

!

!!

!

!

!!!!!!

!!!!

!

!

!

!

!

!

! !

!

!

!!!

!!!!!

!

!

!

!

!

!

!! !!!

!!

!!

!!

!

!

!!

!

!

!

!

!

!!

!!!! !

!

!! !!

!!

!!! !

!

!!

!

!

!!

!!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!!!

!

!!!

!

!

!!!!

!

!

!

!!

!

!

!!

!

!!!!!!

!!!

! !

!

!!!!

!!

!!!

!!

!

!!!

!

!

!

!

!

!!

!

!

!!

!

!

!

!!!

!

! !

!

!

!

!

!!

!

!!

!!

!

!

! !!!

!

!

!

!!

!!

!!!! !!!!!

!

!

!

!!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!!! !!

!

!!! !!

!

!

!

!

!

!

!

!

!

!

!!

!!

!

!

!

!

!!!

!

!

!

!

!

!!

!

!!

!

!!!!

!!

!

!

!

!!!!

!!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!!

!!

!

!

!

!

!

! !!!!! !

!

!

!

!

!!!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!!

!

!

!

!

!!!

!

!

!

!

!!

!

!

!

!!

!

!

!

!

!!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!!

!

!

!

!!

!

!

!

!

!

! !

!!

!

!! !

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

! !

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!!

!

!

!

!!

!

!

!

!!

!!

!

!!

!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!!!

!

!!!!

!

!!

!

!

!

!

!

!

!

!

!!!

!

!

!!

!

Land Use

University and College Campuses

Central Business District

National Parks

Bodies of Water

Boundary of Washington D.C.

Locations of Services

! Museums

! Marketplaces

! Bars and Restaurants

Study Area0 5 102.5 Kilometers

0 2.5 51.25 Miles ´

DATA

Six Full Days in 2019

Sunday May 12thMonday May 13thTuesday May 14thThursday May 16thSaturday June 1st Friday June 14th

Sources

BirdJumpLimeLyftSkip Spin

APIs - publicly accessible via DDOT

Unit of Analysis

Geographic

.07 square miles = .19 square kilometers

*p<0.05, **p<0.01, ***p<0.001Statistical Significance

Inputs

Spatial:LatitudeLongitude

Temporal:Timestamp5 minute - intervals

1671fishnets

x 6days

x 24hours

= 240,624observations

Model 1 Model 2 Model 3 Model 4

O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig.

Weekend Day 0.79 * -0.26 * -0.16 *** -0.31 **

12AM – 6AM 0.58 *** -0.82 *** -0.41 *** N/A N/A

6AM – 12PM 0.65 *** 0.21 -0.03 N/A N/A

12PM – 6PM 0.88 0.68 *** 0.04 N/A N/A

Model 1 Model 2 Model 3 Model 4

O.R. Sig. Coefficient Sig. Coefficient Sig. Coefficient Sig.

Census Tract Population

Density (1000s)1.13 *** 0.02 *** 0.00 ** -0.02 ***

Low Income Area 9.58 *** 0.27 0.05 -0.39 *

Low–Medium Income Area 11.22 *** 0.35 * 0.09 -0.39 *

High–Medium Income Area 17.33 *** 0.05 0.04 -0.25

Number of Museums 1.44 0.64 *** 0.22 *** -0.14

Number of Marketplaces 2.15 *** -0.31 *** -0.07 ** -0.16 *

Number of Bars & Restaurants 1.16 *** 0.23 *** 0.05 *** -0.03 ***

Part of the CBD 25.36 *** 3.57 *** 1.00 *** -0.63 ***

Part of a College Campus 2.28 *** -0.13 -0.01 -0.10

Part of a National Park 1.12 0.14 ** 0.06 ** 0.06

INTRODUCTIONINTRODUCTION

Weekends & late nights: fewer e-scooters & less variation in hourly e-scooter presence.

Higher population density & being located inthe CBD: more e-scooters, contributed to more change in the hour-to-hour numbers of e-scooters, but less variation throughout the day.

Bikeshare stations & bicycle lanes: positively impacted the presence & hourly change in e-scooters, low variation throughout the day.

Metro stations: positively impacted the average number of e-scooters in an area, and hourlymovement to & from an area, not a significant indication of presence.

There is a relationship between publictransport & e-scooters, it is not clear if they serve as a first-mile last-mile solution.

The models suggest that e-scooters were availblenear bike lanes.

Our study examines the factors thatdetermine the presence of e-scooters, as well as those that cause variation in e-scooter presence between each consecutive hour and throughout the day.

Data on the location of e-scooters inthe Washington D.C. area over six full days was collected.

Then, multi-level mixed effects linear regression models were generated to investigate the impact of time, land use characteristics, andtransportation infrstructure while controlling for weather conditions.

This study contributes to a more comprehensive understanding of the factors that impact the presence as well as variations in the presence of e-scooters using data obtained for e-scooters operating in Washington D.C.

Dataset cannot address if an e-scooter was placed as part of rebalancing or by a user.

Utilization patterns can help city planners & officials understand how shared e-scooters areused and how they interact with existing systems.

Leila Hawa, Boer Cui,Lijun Sun & Ahmed El-Geneidy McGill University

Determinants of shared electric scooter use in Washington D.C.Scoot over:

12:00 AM - 1:00 AM 6:00 AM - 7:00 AM

12:00 PM - 1:00 PM 6:00 PM - 7:00 PM

E-Scooters per Fishnet

0 - 2

3 - 8

9 - 16

17 - 2

6

27 - 4

9

Metro Lines

Central Business District

Bodies of Water

Transportation Research at McGillRAMT

Distribution of e-scooters at selectedperiods of the day

Coordinate System: GCS WGS 1984Datum: WGS 1984Units: DegreeSources: District Department of Transportation ´

0 5 102.5 Kilometers

0 2.5 51.25 Miles

SDN Japan | Service Business Model SIG Vol. 1

SDN Japan | Service Business Model SIG Vol. 1

Design
The SIG Messerschmitt Bf-109 ARF is a sport scale model, not · The SIG Messerschmitt Bf-109 ARF is a sport scale model, not necessarily based on any particular full-scale variant

The SIG Messerschmitt Bf-109 ARF is a sport scale model, not · The SIG Messerschmitt Bf-109 ARF is a sport scale model, not necessarily based on any particular full-scale variant

Documents
SIG Online Tool For Transformation Model

SIG Online Tool For Transformation Model

Documents
HL7 Clinical-Genomics SIG: A Shared Genotype Model HL7 V3 Compliant HL7 Clinical-Genomics SIG Facilitator Amnon Shabo (Shvo) IBM Research Lab in Haifa

HL7 Clinical-Genomics SIG: A Shared Genotype Model HL7 V3 Compliant HL7 Clinical-Genomics SIG Facilitator Amnon Shabo (Shvo) IBM Research Lab in Haifa

Documents
Variable Coefficient Inductance Model-Based Four-Quadrant

Variable Coefficient Inductance Model-Based Four-Quadrant

Documents
Receiving water quality model and water quality objectives · C urban = representative coefficient for an entirely urban catchment C non-urban = representative coefficient for an

Receiving water quality model and water quality objectives · C urban = representative coefficient for an entirely urban catchment C non-urban = representative coefficient for an

Documents
SIG Pistols P 210 - Forgotten Weapons Pistols P21… · Swiss Industrial Company Neuhausen Rhine Falls Switzerland SIG Pistols P 210 SIG pistol P 210-1 Standard model with wooden

SIG Pistols P 210 - Forgotten Weapons Pistols P21… · Swiss Industrial Company Neuhausen Rhine Falls Switzerland SIG Pistols P 210 SIG pistol P 210-1 Standard model with wooden

Documents
SUBDIFFUSION MODEL WITH TIME-DEPENDENT …thermalscience.vinca.rs/pdfs/papers-2016/TSCI160427247H.pdf · Hristov, J.: Subdiffusion Model with Time-Dependent Diffusion Coefficient

SUBDIFFUSION MODEL WITH TIME-DEPENDENT …thermalscience.vinca.rs/pdfs/papers-2016/TSCI160427247H.pdf · Hristov, J.: Subdiffusion Model with Time-Dependent Diffusion Coefficient

Documents
Single-index Varying Coefficient Model for Functional Responsesadni.loni.usc.edu/adni-publications/Single-index varying... · 2019. 6. 4. · Single-index Varying Coefficient Model

Single-index Varying Coefficient Model for Functional Responsesadni.loni.usc.edu/adni-publications/Single-index varying... · 2019. 6. 4. · Single-index Varying Coefficient Model

Documents
Outsourcing Governance Model - SIG

Outsourcing Governance Model - SIG

Documents
Cognitive Neuropsychology Model of Communication - AAC sig

Cognitive Neuropsychology Model of Communication - AAC sig

Documents
MONDAY, SEPTEMBER 9, 2013 (PAGE 12) DAILY EXCELSIOR, …epaper.dailyexcelsior.com/epaperpdf/13sept09/page12.pdf · jadu mohan krishnapada pal 5 sig 2 3 sig 4 sig 5 sig 3 sig sig 5

MONDAY, SEPTEMBER 9, 2013 (PAGE 12) DAILY EXCELSIOR, …epaper.dailyexcelsior.com/epaperpdf/13sept09/page12.pdf · jadu mohan krishnapada pal 5 sig 2 3 sig 4 sig 5 sig 3 sig sig 5

Documents
North Carolina Cancer Incidence, 200 -2005, All Sites, By ...Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate

North Carolina Cancer Incidence, 200 -2005, All Sites, By ...Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate Sig. Cases Rate

Documents
Materi 5 Sumber & Model Data dalam SIG - …parno.staff.gunadarma.ac.id/.../GIS_05+Sumber+dan+Model+Data+GIS.pdf · 1 Materi 5 Sumber & Model Data dalam SIG 2 SIG: Data Input & Output

Materi 5 Sumber & Model Data dalam SIG - …parno.staff.gunadarma.ac.id/.../GIS_05+Sumber+dan+Model+Data+GIS.pdf · 1 Materi 5 Sumber & Model Data dalam SIG 2 SIG: Data Input & Output

Documents
A Functional Varying-Coefficient Single-Index Model for

A Functional Varying-Coefficient Single-Index Model for

Documents
A MODIFICATION OF EFFICACY COEFFICIENT MODEL FOR

A MODIFICATION OF EFFICACY COEFFICIENT MODEL FOR

Documents
Drag Coefficient Prediction for Model Rockets

Drag Coefficient Prediction for Model Rockets

Documents
Flowsheet simulation of cobalt–nickel separation by ... · 2.2. Distribution Coefficient Models A distribution coefficient model predicts the distribution of species between the

Flowsheet simulation of cobalt–nickel separation by ... · 2.2. Distribution Coefficient Models A distribution coefficient model predicts the distribution of species between the

Documents
RESEARCH ARTICLE Open Access Three-dimensional ... · CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient

RESEARCH ARTICLE Open Access Three-dimensional ... · CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient

Documents
EHR Functional Model and Standard Health Level Seven (HL7) EHR SIG Gary Dickinson, Linda Fischetti, Sam Heard SIG Co-Chairs

EHR Functional Model and Standard Health Level Seven (HL7) EHR SIG Gary Dickinson, Linda Fischetti, Sam Heard SIG Co-Chairs

Documents
On cutting force coefficient model with respect to tool ... · On cutting force coefficient model with respect to tool geometry and tool wear Petr Kolar1*, Petr Fojtu1and Tony Schmitz2

On cutting force coefficient model with respect to tool ... · On cutting force coefficient model with respect to tool geometry and tool wear Petr Kolar1*, Petr Fojtu1and Tony Schmitz2

Documents
A Cfd model to predict pressure loss coefficient in

A Cfd model to predict pressure loss coefficient in

Documents
Random-coefficient Models 1. Markov-switching model (MRS) 2. Threshold model 3. Quantile Regression model (QR)

Random-coefficient Models 1. Markov-switching model (MRS) 2. Threshold model 3. Quantile Regression model (QR)

Documents
TUTORIAL Solar Module Physical Model - PSIM Software · TUTORIAL Solar Module Physical Model OCTOBER 2016 ... Saturation Current Is0: ... coefficient Ct)

TUTORIAL Solar Module Physical Model - PSIM Software · TUTORIAL Solar Module Physical Model OCTOBER 2016 ... Saturation Current Is0: ... coefficient Ct)

Documents
sigdent catalog for print8mm SIG-F4308 SIG-F5108 SIG-F6108 10mm SIG-F4310 SIG-F5110 SIG-F6110 11.5mm SIG-F4311 SIG-F5111 SIG-F6111 13mm SIG-F4313 SIG-F5113 SIG-F6113 16mm …

sigdent catalog for print8mm SIG-F4308 SIG-F5108 SIG-F6108 10mm SIG-F4310 SIG-F5110 SIG-F6110 11.5mm SIG-F4311 SIG-F5111 SIG-F6111 13mm SIG-F4313 SIG-F5113 SIG-F6113 16mm …

Documents
Model-Driven SOA OMG SOA SIG Activity Debrief June 28 th 2007 Dr. Harsh W. Sharma, Co-Chair, OMG SOA SIG James Odell, Co-Chair, OMG SOA SIG Pete Rivett,

Model-Driven SOA OMG SOA SIG Activity Debrief June 28 th 2007 Dr. Harsh W. Sharma, Co-Chair, OMG SOA SIG James Odell, Co-Chair, OMG SOA SIG Pete Rivett,

Documents
Baseline Analyses of SIG Applications and SIG Eligible and SIG

Baseline Analyses of SIG Applications and SIG Eligible and SIG

Documents
The Effectiveness of Implementing Sterilization for Thailand ......Chapter 5 demonstrates the econometric model specification for the sterilization coefficient and offset coefficient

The Effectiveness of Implementing Sterilization for Thailand ......Chapter 5 demonstrates the econometric model specification for the sterilization coefficient and offset coefficient

Documents
I/O Virtualization and Sharing - uml.eduweblab.cs.uml.edu/~bill/cs520/slides_15E_PCI_Express_IOV.pdf · SW Model HW Model. PCI-SIG Developers Conference Copyright © 2006, PCI-SIG,

I/O Virtualization and Sharing - uml.eduweblab.cs.uml.edu/~bill/cs520/slides_15E_PCI_Express_IOV.pdf · SW Model HW Model. PCI-SIG Developers Conference Copyright © 2006, PCI-SIG,

Documents
A Correlation Coefficient based Model to Separate and ... · A Correlation Coefficient based Model to Separate and Classify Noncursive (Grantha Script) Symbols Lalit Prakash Saxena

A Correlation Coefficient based Model to Separate and ... · A Correlation Coefficient based Model to Separate and Classify Noncursive (Grantha Script) Symbols Lalit Prakash Saxena

Documents