Estimating the Total Number of Workplace and Public Electric Vehicle Chargers in California

Abstract

Allocating charging infrastructure to support the plug-in electric vehicle (PEV) market transition is a major challenge for policy makers and planners seeking to promote PEV adoption. Most electric vehicle charging away from home takes place at or near the workplace, but existing research is focused on public charging. This study developed a method to combine charging location data from a variety of sources including users’ self-reports to produce a combined database of workplace and public charging locations. By matching survey responses to charging locations reported in other datasets using spatial clustering methods, we estimated the number of charging locations and chargers in California. This analysis found that charger location datasets from the Alternative Fuels Data Center and Plugshare included many of the same charging locations as each other but missed many chargers that serve commuters; as a result, existing datasets significantly underrepresent the total number of chargers in California. Some 28,175 Level 2 chargers and 7,350 direct current fast chargers by were identified by combining charger location data from the Alternative Fuels Data Center and Plugshare and at least 27,000 additional chargers near commute destinations by adding locations reported in a survey of electric vehicle drivers.

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References

Lee

J. H.

Chakraborty

Hardman

S. J.

Tal

Exploring Electric Vehicle Charging Patterns: Mixed Usage of Charging Infrastructure. Transportation Research Part D: Transport and Environment, Vol. 79, 2020, p. 102249. https://doi.org/10.1016/j.trd.2020.102249

Hardman

Jenn

Tal

Axsen

Beard

Daina

Figenbaum

Jakobsson

Jochem

Kinnear

Plötz

Pontes

Refa

Sprei

Turrentine

Witkamp

A Review of Consumer Preferences of and Interactions with Electric Vehicle Charging Infrastructure. Transportation Research Part D: Transport and Environment, Vol. 62, 2018, pp. 508–523. https://doi.org/10.1016/j.trd.2018.04.002

US Department of Energy. Alternative Fueling Station Locator. Alternative Fuels Data Center. https://afdc.energy.gov/data/

Nicholas

Tal

Electric Vehicle Fast Charger Planning for Metropolitan Planning Organizations: Adapting to Changing Markets and Vehicle Technology. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2502(1): 134–143. https://doi.org/10.3141/2502-16

Goodchild

M. F.

Citizens as Sensors: The World of Volunteered Geography. GeoJournal, Vol. 69, No. 4, 2007, pp. 211–221. https://doi.org/10.1007/s10708-007-9111-y

Gao

Janowicz

Zhang

Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop. Computers, Environment and Urban Systems, Vol. 61, 2017, pp. 172–186. https://doi.org/10.1016/j.compenvurbsys.2014.02.004

Elwood

Goodchild

M. F.

Sui

D. Z.

Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, Vol. 102, No. 3, 2012, pp. 571–590. https://doi.org/10.1080/00045608.2011.595657

Flanagin

A. J.

Metzger

M. J.

The Credibility of Volunteered Geographic Information. GeoJournal, Vol. 72, No. 3, 2008, pp. 137–148. https://doi.org/10.1007/s10708-008-9188-y

Hecht

Stephens

A Tale of Cities: Urban Biases in Volunteered Geographic Information. Proc., 8th International Conference on Weblogs and Social Media, ICWSM 2014, Ann Arbor, Michigan, United States, 2014.

10.

Brown

Kelly

Whitall

Which ‘public’? Sampling Effects in Public Participation GIS (PPGIS) and Volunteered Geographic Information (VGI) Systems for Public Lands Management. Journal of Environmental Planning and Management, Vol. 57, No. 2, 2014, pp. 190–214. https://doi.org/10.1080/09640568.2012.741045

11.

Zhang

Zhu

A.-X.

The Representativeness and Spatial Bias of Volunteered Geographic Information: A Review. Annals of GIS, Vol. 24, No. 3, 2018, pp. 151–162. https://doi.org/10.1080/19475683.2018.1501607.

12.

Qian

Shi

Cai

Data Cleaning Approaches in Web2.0 VGI Application. Proc., 17th International Conference on Geoinformatics, Fairfax, Virginia, United States, 2009.

13.

Tran

T. N.

Drab

Daszykowski

Revised DBSCAN Algorithm to Cluster Data with Dense Adjacent Clusters. Chemometrics and Intelligent Laboratory Systems, Vol. 120, 2013, pp. 92–96. https://doi.org/10.1016/j.chemolab.2012.11.006.

14.

Sander

Ester

Kriegel

H.-P.

Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 169–194. https://doi.org/10.1023/A:1009745219419.

15.

Ali

Asghar

, and Naseer Ahmed Sajid. Critical Analysis of DBSCAN Variations. Proc., 2010 International Conference on Information and Emerging Technologies. IEEE, 2010.

16.

Gao

Janowicz

Prasad

Extracting and Understanding Urban Areas of Interest Using Geotagged Photos. Computers, Environment and Urban Systems, Vol. 54, 2015, pp. 240–254. https://doi.org/10.1016/j.compenvurbsys.2015.09.001.

17.

PlugShare App. https://company.plugshare.com/plugshare.html. Accessed November 28, 2020.

18.

Tal

Favetti

Nicholas

Exploring the Use of a Web Based Map Tool for Travel Behavior Data Collection: Lessons from Plug-in Vehicle Owner Surveys. Presented at the Transportation Research Board 95th Annual MeetingTransportation Research Board, 2016.

19.

Schubert

Sander

Ester

Kriegel

H.-P.

DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems (TODS), Vol. 42, No. 3, 2017.

20.

OpenStreetMap. OpenStreetMap. https://www.openstreetmap.org/about. Accessed May 7, 2019.