Book ChapterDOI
Smart Parking System to Predict Occupancy Rates Using Machine Learning
Sarthak Garg,Pratyush Lohumi,Supriya Agrawal +2 more
- pp 163-171
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TLDR
In this paper, a study of Frankfurt car parking data was conducted to test several prediction strategies to provide the users with information about occupancy rates of parking lots of both developed and developing nations.Abstract:
Car Parking is a significant issue in urban zones in both developed and developing nations. Following the quick incense of vehicle possession, numerous urban communities are experiencing a lack of car parking regions. Keeping in mind that issue, we undergo the study of Frankfurt Car Parking Data. The aim of this research work is to test several prediction strategies to provide the users with information about occupancy rates of parking lots. With the approach of self-sufficient vehicles as the future and automatic car parking features in cars, realizing the occupancy rates of a parking area beforehand can be valuable and can spare a ton of time and fuel. To predict occupancy rates we are using the following prediction models namely Linear Regression, Neural Networks, Support Vector Regression, Decision Trees (Regression) and Ensemble Decision Trees. We have also implemented K-means clustering as we hypothesise that adding one more feature to the dataset for similar instances would help predictive algorithms to fit better on the data. Using this additional feature, we modified the existing dataset D1 (with 3 features) into D2 (with 4 features). We advocate this hypothesis by comparing the results of prediction algorithms on both datasets (D1 and D2). From the results, we found out XGBoost fits the dataset exceptionally well.read more
Citations
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Journal ArticleDOI
Parking slot occupancy prediction using LSTM
Proceedings ArticleDOI
A Systematic Parking System Using bi-class Machine Learning Techniques
TL;DR: In this paper , a real-time data is used which has been collected from the survey by asking a few questions to the customers who visit the shopping mall to predict the number of different vehicles and the optimal one will be selected and finalized based on the time taken for training and testing, accuracy, and the dataset that is being used.
References
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Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Book
Classification and regression trees
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Book
Neural network design
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
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