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GraphScSh: Efficient I/O Scheduling and Graph Sharing for Concurrent Graph Processing Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Statistical Analysis and Prediction of Parking Behavior

Authors : Ningxuan Feng, Feng Zhang, Jiazao Lin, Jidong Zhai, Xiaoyong Du

Published in: Network and Parallel Computing

Publisher: Springer International Publishing

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Abstract

In China, more and more families own cars, and parking is also undergoing a revolution from manual to automatic charging. In the process of parking revolution, understanding parking behavior and making an effective prediction is important for parking companies and municipal policymakers.
We obtain real parking data from a big parking company for parking behavior analysis and prediction. The dataset comes from a shopping mall in Ningbo, Zhejiang, and it consists of 136,973 records in 396 days. Specifically, we mainly explore the impact of weather factors on parking behavior. We study several models, and find that the random forest model can make the most accurate parking behavior prediction. Experiments show that the random forest model can reach 89% accuracy.

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Metadata
Title
Statistical Analysis and Prediction of Parking Behavior
Authors
Ningxuan Feng
Feng Zhang
Jiazao Lin
Jidong Zhai
Xiaoyong Du
Copyright Year
2019
Book
Network and Parallel Computing

Print ISBN: 978-3-030-30708-0

Electronic ISBN: 978-3-030-30709-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30709-7_8

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