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On-street car parking prediction in smart city: a multi-source data analysis in sensor-cloud environment

conference contribution
posted on 2017-01-01, 00:00 authored by Walaa Alajali, Sheng Wen, Wanlei Zhou
Smart car parking systems in smart cities aim to provide high-quality services to their users. The key to success for smart car parking systems is the ability to predict available car parking lots throughout the city at different times. Drivers can then select a suitable car parking location. However, the prediction process can be affected by many different factors in smart cities such as people mobility and car traffic. This study investigates the use of multi-source data (car parking data, pedestrian data, car traffic data) to predict available car parking in fifteen minute intervals. It explores the relationship between pedestrian volume and demand for car parking in specific areas. This data is then used to predict conditions on holidays and during special events, when the number of pedestrians dramatically increases. A Gradient Boosting Regression Trees (GBRT) is used for prediction. It is an ensemble method that can be more accurate than a single Regression Tree and Support Vector Regression. The probability of error for our model is 0.0291.

History

Event

Guangzhou University and Central South University. Conference (10th : 2017 : Guangzhou, China)

Volume

10658

Series

Guangzhou University and Central South University Conference

Pagination

641 - 652

Publisher

Springer

Location

Guangzhou, China

Place of publication

Cham, Switzerland

Start date

2017-12-12

End date

2017-12-15

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319723945

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2017, Springer International Publishing AG

Editor/Contributor(s)

G Wang, M Atiquzzaman, Z Yan, K Choo

Title of proceedings

SpaCCS 2017 : Proceedings of the 10th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage

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