<div class="csl-bib-body">
<div class="csl-entry">Dey, S., Goel, S., Tomko, M., & Winter, S. (2021). Mapping Parking Spaces Using Crowd-Sourced Trajectories. In S. Winter & S. Goel (Eds.), <i>Smart Parking in Fast-Growing Cities</i> (pp. 184–198). TU Wien Academic Press. https://doi.org/10.34727/2021/isbn.978-3-85448-045-7_13</div>
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Mapping urban parking spaces helps drivers to reduce their search and cruising for parking, thus
reducing traffic, reducing emissions, and reducing total travel times. Mapped urban parking spaces
can also be monitored for real-time occupancy information. But while many cities in Asia, Africa,
and Latin America are experiencing a strong increase of private car use on the roads, they typically
lack such reliable information regarding on-street parking spaces. Hence, in this chapter we explore
globally applicable mapping methods for on-street parking locations, as a first step towards smart
parking (for an alternative approach see Chapter 11).
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dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by-sa/4.0/
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dc.subject
parking space
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dc.subject
parking lot
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dc.subject
mapping
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dc.subject
trajectory
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dc.title
Mapping Parking Spaces Using Crowd-Sourced Trajectories
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dc.type
Book Contribution
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dc.type
Buchbeitrag
de
dc.rights.license
Creative Commons Attribution-ShareAlike 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International